{"id":2042,"date":"2021-10-04T16:47:43","date_gmt":"2021-10-04T16:47:43","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/?page_id=2042"},"modified":"2021-10-12T17:00:29","modified_gmt":"2021-10-12T17:00:29","slug":"committees-choice","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/","title":{"rendered":"Committee’s Choice Sessions"},"content":{"rendered":"\n
\n
\n
\n
\n \n
\n
\n

From the more than 5,500 abstracts submitted for the Annual Meeting program, the organizing committee has curated a collection of sessions identified as \u201cThe Committee\u2019s Choice.\u201d These sessions call attention to important challenges \u2013 climate change and policy, pandemic response, vulnerable populations, disparities and equity in well-being, urban public service, urban sustainability, and diversity, equity and inclusion.<\/p>\n

Click on a link below to jump to that session’s time and location, presentation titles, abstracts, and authors.<\/p>\n

In-person Sessions:<\/strong>
\nTB44: In Person – Committee’s Choice<\/a><\/p>\n

Virtual Sessions:<\/strong>
\nVSA55: Analytics for Policing and Urban Public Service Operations<\/a>
\nVSA60: Modeling and Measuring Insider Threat<\/a>
\nVSA72: Passenger Rail II<\/a>
\nVSB09: Distinguished Lectures: COVID – Production, Supply Chain, Analytics<\/a>
\nVSD54: Diversity\/PSOR\/MIF – Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice I<\/a>
\nVSD78: COVID-19 Response and Disparate Impact<\/a>
\nVMA55: Diversity\/PSOR\/MIF – Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice II<\/a>
\nVMA78: Relief Distribution Strategies for Vulnerable and Hard to Reach Populations<\/a>
\nVMB09: Panel: Communicating with Stakeholders in Health<\/a>
\nVMB78: NSF Rapids Related to Covid<\/a>
\nVMC39: Social-Ecological-Technological Systems (SETS) Resilience for Urban Sustainability in the Anthropocene<\/a>
\nVMC71: Passenger Rail I<\/a>
\nVMD47: Green Finance<\/a>
\nVMD78: Understanding Disparities and Equity in Well-being through Data-Driven Approaches<\/a>
\nVTA47: Green Finance 2<\/a>
\nVTA79: Panel: Health in Low and Middle Income Countries (LMICs)<\/a>
\nVWA54: Operations Research & Vulnerable Populations<\/a><\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n
\n

In-person Sessions<\/h2> <\/div>\n<\/div>\n <\/div>\n
\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n TB44: In Person - Committee's Choice <\/span>\n\n \n \n Tuesday, October 26, 7:45-9:15am PDT
CC - Room 304B <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Can\u2019t Wait: Reducing Treatment Delay For Psychiatric Patients<\/h5>\n

Hospital emergency departments (ED) are often heavily backlogged by patients in need of care but awaiting placement in an inpatient bed (IP) either at their current hospital or transfer to another facility. This is known as ED boarding and disproportionately affects patients requiring psychiatric care and to a greater extent, its subpopulation of pediatric patients. Our goal is to find novel modifications for the current system that are effective in reducing ED boarding due to lack of available IP beds, distance-related transfer restrictions, and patient-characteristic related inclusion and exclusion criteria that minimize disparities by age and geographic region.<\/p>\n

Nathan Adeyemi1<\/sup>, Nasibeh Zanjirani Farahani2<\/sup>, Amanda Graham2<\/sup>, Kalyan Pasupathy2<\/sup>, Kayse Lee Maass1<\/sup>; 1<\/sup>Northeastern University, USA, 2<\/sup>Mayo Clinic, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Design Of Staged Alert Systems For COVID-19<\/h5>\n

Judicious implementation and relaxation of pandemic restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. We describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in Austin, Texas through the COVID-19 pandemic, acknowledging inequities, and accounting for an exit strategy under effective vaccines.<\/p>\n

David Morton1<\/sup>, Nazlican Arslan2<\/sup>, Daniel Duque1<\/sup>, Bismark Singh3<\/sup>, Ozge Surer1<\/sup>, Haoxiang Yang4<\/sup>, Lauren Meyers5<\/sup>; 1<\/sup>Northwestern University, USA, 2<\/sup>northwestern university, USA, 3<\/sup>Friedrich-Alexander Universitat, Germany, 4<\/sup>Los Alamos National Laboratory, USA, 5<\/sup>University of Texas, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Simpler (classical) And Faster (quantum) Algorithms For Gibbs Partition Functions<\/h5>\n

We consider the problem of approximating the partition function of a classical Hamiltonian using simulated annealing. This requires the computation of a cooling schedule, and the ability to estimate the mean of the Gibbs distributions at the corresponding inverse temperatures. We propose classical and quantum algorithms for these two tasks, achieving two goals: (i) we simplify the seminal work of \u0160tefankovi\u010d, Vempala and Vigoda (J. ACM, 56(3), 2009), improving their running time and almost matching that of the current classical state of the art; (ii) we quantize our new simple algorithm, improving upon the best known algorithm for computing partition functions of many problems, due to Harrow and Wei (SODA 2020). A key ingredient of our method is the paired-product estimator of Huber (Ann. Appl. Probab., 25(2),2015).<\/p>\n

Srinivasan Arunachalam1<\/sup>, Vojtech Havlicek2<\/sup>, Giacomo Nannicini2<\/sup>, Kristan Temme2<\/sup>, Pawel Wocjan2<\/sup>; 1<\/sup>IBM, USA, 2<\/sup>IBM T.J. Watson, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n
\n

Virtual Sessions<\/h2> <\/div>\n<\/div>\n <\/div>\n
\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VSA39: Modeling the Environmental Impacts of Emerging Transportation Technologies and Systems <\/span>\n\n \n \n Sunday, October 24, 6-7:30am PDT
Virtual Room 39 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Modeling Greenhouse Gas Impacts Of Vehicle Electrification In The Us And China<\/h5>\n

As the largest two carbon emitters, the United States and China have pledged to achieve carbon neutrality by 2050 and 2060, respectively. Vehicle electrification is now widely viewed as an inevitable part of the roadmap. However, it is not yet clear to what necessary or realistic extent, vehicle electrification can contribute to the carbon neutrality goals in the two countries. In this study, we address this question by using MA3T and NEOCC to simulate adoption of electric vehicles in the two countries and considering consumer acceptance, supply decision, vehicle efficiency, battery cost, charging infrastructure, and policy forcing. The resulting greenhouse gas emissions are calculated with consideration of electricity carbon intensity over time under different grid decarbonization scenarios.<\/p>\n

Zhenhong Lin; Oak Ridge National Laboratory, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Incentives Design Optimization For Plug-in Electric Vehicles Diffusion And Target Emissions Reduction<\/h5>\n

Electric vehicles (EVs) are expected to reduce environmental externalities of transportation and contribute to the sector\u2019s decarbonization. We aim to increase EV adoption with monetary incentives (i.e., rebates and charging infrastructure investments) and achieve a target emissions reduction. A mathematical model is proposed to optimize the budget allocation of charging infrastructure deployment and EV purchase rebates over a set planning horizon. We implement a metaheuristic algorithm based on simulated annealing to solve the nonlinear programming problem. To provide comprehensive policy recommendations, we conduct sensitivity analyses to evaluate changes in the investment portfolio.<\/p>\n

Yen-Chu Wu, Eleftheria Kontou; University of Illinois at Urbana-Champaign, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Emission Reduction Potentials Of Shared Micro-mobility In Global Cities<\/h5>\n

The operation of shared micro-mobility systems includes rebalancing and charging processes, which leads to additional greenhouse gas (GHG) emissions. Shared micro-mobility systems can only reduce emissions when replacing car trips or integrating with transit. But the system operation relies on local travel patterns and the mode replacement relies on travel behavior. We will use a data-driven framework to assess GHG emissions of shared micro-mobility, which includes a life cycle assessment model for emission generation and a simulation model for emission reduction based on mode replacement. We will apply the model to global cities and compare different systems based on real-world travel patterns and riders\u2019 travel behaviors.<\/p>\n

Hao Luo; USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
High-impact Locations For Electric Vehicle Charging Based On Travel Behaviors And Technology Limitations<\/h5>\n

In this paper we examine travel behaviors and technological capabilities to identify high-impact locations for locating electric vehicle charging stations. We compare potential charging locations based on their ability to serve vehicle charging needs without interrupting personal travel schedules throughout the day and year. Charging locations are also compared alongside vehicle costs and battery capacities to evaluate the extent to which they provide equitable access to electric vehicles. Finally, we examine how technology improvement trajectories can be accounted for in technology and policy planning for expanding electric vehicle charging.<\/p>\n

Jessika Trancik, Wei Wei; Massachusetts Institute of Technology, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VSA55: Analytics for Policing and Urban Public Service Operations <\/span>\n\n \n \n Sunday, October 24, 6-7:30am PDT
Virtual Room 55 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Data-driven Optimization For Atlanta Police Zone Design<\/h5>\nWe present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload among geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incidents reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high priority 911 calls by 5.8% and the imbalance of police workload among different zones by 43%.\n\nShixiang Zhu; Georgia Institute of Technology, USA <\/div>\n<\/div>\n <\/div>\n
\n \n
\n
\n
Reinforcement Learning For Fair Police Dispatch <\/h5>\n

We propose a reinforcement learning framework to determine the optimal dispatch policy for policing that takes into account proper and fair patrol coverage. Integrating results from patrol car GPS trajectory data provided by the Atlanta Police Department, we develop simulations modeling a real-world crime and policing environment. An agent treating this simulation as a Markov decision process will learn how to best dispatch individual patrollers to incidents. We will use a cross-entropy regularizer weighted by population and census demographics to prevent unfair, unbalanced policing that favors certain demographics. Maximizing the coverage of patrollers allows for faster response than greedy dispatch of the nearest car.<\/p>\n

Alexander Bukharin; Georgia Tech, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Designing A General Framework For Solving School Bus Routing Problems<\/h5>\n

School bus routing is a crucial challenge faced by many school districts. While advanced algorithms exist in the literature, they can be difficult for districts to implement and commercial software can yield poor solutions. Further, bus transportation impacts many aspects of education, so the \u201coptimal\u201d solution is often hard to define. In this work, we aim to develop a simple framework for optimizing transportation problems, starting from data analysis to optimization via visualization. We\u2019ll make use of well-developed tools such as Google OR-Tools, which can already provide near-optimal solutions. Specifically, we provide user-friendly visualizations to help decision maker understand their overall system and solutions, presenting results from a collaboration with Denver Public Schools as a case study.<\/p>\n

Min Fei, Karen Smilowitz, Sebastien Martin; Northwestern University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Optimizing Shift Schedules And Dispatch Of Safety Patrol Officers For Denver Public Schools<\/h5>\n

Each year, the Safety Department at Denver Public Schools (DPS) manually creates patrol officer schedules to respond to calls for over 200 schools to ensure the safety of all students and staff. The Safety Department struggles to adjust schedules in response to changes such as available officers due to the manual process. To address these drawbacks, we developed optimization and simulation models to create officer shift schedules based on call demand, factor in call demand uncertainty, and estimate the performance of the generated schedules. The DPS Safety Department used one of multiple generated schedules for the 2019-2020 academic year and we able to meet their target call response times.<\/p>\n

Amanda Chu, Pinar Keskinocak, Onkar Kulkarni, Ritesh Ojha; ISyE Georgia Institute of Technology, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VSA60: Modeling and Measuring Insider Threat <\/span>\n\n \n \n Sunday, October 24, 6-7:30am PDT
Virtual Room 60 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Mitigating Behavior Of Poll Workers As An Insider Threat To Elections Security<\/h5>\n

Poll workers are on the first line of defense in elections security and are trusted insiders to the process. This research assesses personal computer security behaviors for poll workers using the Security Behavior Intentions Scale (SeBIS) survey and a sample of 2,213 poll workers from 13 states. An information theory model is developed to examine potential weaknesses in security behaviors and identify security practices to improve with poll workers. Outcomes from this research aid in identifying a poll worker who may pose an insider threat and mitigating unintentional consequence.<\/p>\n

Natalie M. Scala, Josh Dehlinger, Yeabsira Mezgebe; Towson University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Neural Network Detection for Insider Threats<\/h5>\n

Insider threats present one of the largest threats to defense capabilities and readiness, but their cost is difficult to value and frequently goes undiscussed. This presentation will address several operations research applications to the prevention, detection, and mitigation of insider threats within the Army, informed by the cadet’s work with the Army G-3\/5\/7 at the Pentagon.<\/p>\n

Brooke Allen; United States Military Academy, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Optimization of the DoD Insider Threat Program<\/h5>\n

Pier Bos; United States Military Academy, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VSA72: Passenger Rail II <\/span>\n\n \n \n Sunday, October 24, 6-7:30am PDT
Virtual Room 72 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Rolling Stock Allocation And Timetabling For Rail Transit Network With Multiple Depots<\/h5>\n

Rolling stock management and service timetabling are two challenging but very related issues in urban rail networks. The former is to allocate a certain fleet of trains to each depot, while the latter designs the arrival\/departure times of train services according to the available trains at each depot. In this study, we develop an integer linear programming (ILP) to jointly optimize the allocation of rolling stocks and train timetables for the involved lines in an urban rail network. Considering that the problem with a large time horizon involves numerous integer variables which cannot be handled efficiently, a Benders decomposition method is introduced to decompose our ILP model (whole network) into a group of small subproblems (each line). Furthermore, numeric experiments derived from the Beijing rail transit network are conducted to validate our model and algorithm.<\/p>\n

Fan Pu; Beijing Jiaotong University, China<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Timetable Optimization For Minimizing Transfer Costs Under Through-operation<\/h5>\n

In public passenger rail systems, passengers often need to make several transfers to reach their destination, which leads to increased travel time and safety risks. In this paper, we adopt a through-operation<\/i> model, which allows trains from different lines to travel on other lines, and propose a mixed integer programming model. By adjusting trains\u2019 dispatch times and headways at the stations, the model can obtain an optimal timetable, providing the minimum dwell time for on-board passengers and minimizing the transfer waiting time for all transfer passengers. The proposed model are validated by some numerical experiments based on the operational data derived from the Beijing urban rail network.<\/p>\n

Yi Zheng; Peking University, China<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Dynamic Passenger-centric Railway Traffic Management<\/h5>\n

Railway traffic management refers to the monitoring and rescheduling of train services that are affected by disturbances. In practice, traffic management relies on traffic controllers who manually adjust train services and do not have any tools to consider passenger needs. Thus, minimizing train delays is their main goal for traffic management. However, train delays differ from passenger delays that also depend on other factors like the path choice of passengers. Hence, I will show a recent work on Passenger-Oriented Railway Traffic Management (PORTM), which focuses on the impact of decisions on passengers. The PORTM problem is formulated into a MILP model that integrates timetable rescheduling and passenger assignment. The model dynamically reschedules the timetable for disturbances that emerge over time aiming to minimize passengers’ generalized travel times.<\/p>\n

Yongqiu Zhu; ETH Zurich, Switzerland<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Trains Rescheduling Method During Multi-disturbances Under A Quasi-moving Block System<\/h5>\n

It is critical to reschedule trains timely in Chinese high-speed railway system. This paper presents a rescheduling model which can deal with multi-disturbances by retiming, reordering, rerouting and train speed adaptation under a quasi-moving block system. The alternative arcs and alternative arrival\/departure paths are designed in constraints to figure out the siding lines blockage. A custom-designed two-step method based on a commercial solver is applied to solve instances from Chinese high-speed networks quickly. The experimental cases with\/without the siding line blockage are studied respectively. The outputs demonstrates that the proposed approach can achieve a reduction of train delays by 70% compared to the solution without reordering, and the application of resetting routes can mitigate traffic tardiness and speed up rescheduling process effectively.<\/p>\n

Peijuan Xu1<\/sup>, Francesco Corman2<\/sup>; 1<\/sup>Chang’an University, China, 2<\/sup>ETH Zurich, Switzerland<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Train Scheduling Optimization With Virtual Coupled Vehicles For Integrated Commuter Rail And Underground Metro Network<\/h5>\n

With the increasing of passenger demand and the saturated operations of trains, the planning process is attracting more and more attention. In this paper, we study the integration of train scheduling and virtual coupling planning under various passenger demands for integrated commuter rail and underground metro network, where the practical train operation constraints, e.g., the number of available rolling stocks, the running time of different train composition and the entering\/exiting depot operations, are considered. In order to optimize the train scheduling, we extend a job-shop scheduling model to a MINLP model. We propose an equivalent MILP formulation and develop a Benders’ decomposition algorithm to improve the computational efficiency. Two sets of numerical experiments are conducted to verify the effectiveness and efficiency of our practical methods.<\/p>\n

Simin Chai; Beijing Jiaotong University, China<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VSB09: Distinguished Lectures: COVID - Production, Supply Chain, Analytics <\/span>\n\n \n \n Sunday, October 24, 7:45-9:15am PDT\nVirtual Room 09 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
COVID-19, the Global Supply Chain for Medical Products, and Operations Research<\/h5>\n

The supply chain for medical products including PPE, vaccines, testing supplies and therapeutics has faced intense public scrutiny in the last 18 months. Companies with vaccine, therapeutic, and diagnostic technologies have to make their future supply chain network design decisions against this backdrop of high policy and demand uncertainty, intense public scrutiny, and strong demands for meeting their social contract. This talk will share a few examples of how operations research models which are deeply embedded in context can help both public agencies and individual firms in making decisions related to health product purchasing, portfolio design, network design and manufacturing flexibility. It will also include findings from work on healthcare supply chain design for future pandemic preparedness.<\/p>\n

Prashant Yadav; INSEAD, France<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Supply Chain Resiliency and the Need for Stress-Tests<\/h5>\n

As hospitalizations decrease and vaccinations increase, some see the end of the COVID-19 pandemic somewhere on the horizon. With that, many of us in the data science community are analyzing \u201clessons learned\u201d from the pandemic to better prepare and more efficiently and effectively respond to the next disaster. At the center of this discussion must be how to fix our supply chains to prevent disruptions where possible and to identify – before a disaster occurs – where vulnerabilities exist. This is the focus of my presentation.<\/p>\n

David Simchi-Levi; Massachusetts Institute of Technology, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VSD54: Diversity\/PSOR\/MIF - Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice I <\/span>\n\n \n \n Sunday, October 24, 2:45-4:15pm PDT\nVirtual Room 54 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
We\u2019re Here: Interviews With LGBTQ+ Members Of The INFORMS Community<\/h5>\n

While it can be tempting to rely solely on quantitative metrics, it is also critical to humanize individuals when it comes to minority issues. This requires stories to be told, heard, and documented. The objective for this project is to use semi-structured interviews to survey, document, and report the individual stories that color and humanize data for LGBTQ+ issues. Choosing to be \u201cout\u201d in academia is a highly personal and nuanced decision, and it is one that is unique to the LGBTQ+ community. Where do ambitious students or early career faculty find an LGBTQ+ mentor in our field? What mentorship advice can be condensed and shared publicly? The aim of this work is to tackle these and other challenges with a document that is meant to be valuable for Queer and non-Queer audiences, alike. This is a work in progress sponsored by the INFORMS DEI Ambassador Program.<\/p>\n

Tyler Perini; Georgia Institute of Technology, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Challenges Faced By Black Applicants To Graduate Programs In Computing<\/h5>\n

While AI has revolutionized many sectors in society, Black people remain underrepresented within the field. In this talk, we describe the Black in AI Academic Program, a program that supports black researchers as they apply to graduate programs, navigate graduate school, and enter the postgraduate job market. We support our applicants with online information sessions, resource documents, and mentorship. We examine the impact of mentorship and the challenges that the 2019-2020 cohort in the program encountered during the application process. Overall 56% of the program participants were successfully admitted, but the lack of information, financial constraints, and unclear academic systems remain the prominent challenges that black people face in applying for AI graduate programs. We discuss the implications and offer recommendations to alleviate these challenges.<\/p>\n

Ezinne Nwankwo; University of California, Berkeley, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Diversity and Inequality in Social Networks<\/h5>\n

Online social networks often mirror inequality in real-world networks. Such disparities are often amplified by algorithms that leverage social data to provide recommendations, share information or form groups. I review explanations for algorithmic bias in social networks, addressing information diffusion, grouping, and general definitions of inequality. I use network models that reproduce inequality seen in online networks to characterize the relationship between pre-existing bias and algorithms in creating inequality, discussing different algorithmic solutions for mitigating bias. I address challenges in bridging theory and practice in studying bias and inequality.<\/p>\n

Ana-Andreea Stoica; Columbia University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Centering Racial Equity In Research And Data Practice<\/h5>\n

Creating equitable data practices produces meaningful insights across racial groups while avoiding the common pitfalls that result in harmful data work. Legacies of discrimination can show up in data, and research practices themselves can be exploitative. Centering racial equity is not about avoiding data bias or difficult research questions but focuses on acknowledging that these challenges exist and how to create solutions for tackling them. This talk will cover some of the ways to practice racially aware data collection, analysis, and research. I will highlight high-level considerations that extend to data work with underserved communities broadly and offer resources for further development of equitable data practices.<\/p>\n

Alex Jackson; Carnegie Mellon University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VSD78: COVID-19 Response and Disparate Impact <\/span>\n\n \n \n Sunday, October 24, 2:45-4:15pm PDT\nVirtual Room 78 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Structural Racism And Covid-19 Response: Higher Risk Of Exposure Drives Disparate Covid-19 Deaths Among Black And Hispanic\/Latinx Residents Of Illinois, USA<\/h5>\n

In 2020, Black and Latinx communities across the United States, including in Illinois, experienced disproportionately high rates of COVID-19 cases and deaths. Public health officials implemented targeted programs to increase intervention access and reduce disparities. Data on SARS-CoV-2 diagnostic tests, COVID-19 cases, and COVID-19 deaths were used to quantify the evolution of disparities in Illinois. 79.3% and 86.7% of disparities in deaths among Black and Latinx populations respectively were attributable to differences in incidence compared to White populations rather than differences in case fatality ratios. Relative lack of access to health care, crowded living conditions, and high-risk occupations are the result of structural racism, which placed Black and Latinx populations at higher risk of exposure to SARS-CoV-2 and higher COVID-19 mortality.<\/p>\n

Jaline Gerardin; Northwestern University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Design Of Staged Alert Systems For COVID-19<\/h5>\n

Judicious implementation and relaxation of pandemic restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. We describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in Austin, Texas through the COVID-19 pandemic, acknowledging inequities, and accounting for an exit strategy under effective vaccines.<\/p>\n

David Morton1<\/sup>, Nazlican Arslan2<\/sup>, Daniel Duque1<\/sup>, Bismark Singh3<\/sup>, Ozge Surer1<\/sup>, Haoxiang Yang4<\/sup>, Lauren Meyers5<\/sup>; 1<\/sup>Northwestern University, USA, 2<\/sup>northwestern university, USA, 3<\/sup>Friedrich-Alexander Universitat, Germany, 4<\/sup>Los Alamos National Laboratory, USA, 5<\/sup>University of Texas, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VMA55: Diversity\/PSOR\/MIF - Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice II <\/span>\n\n \n \n Monday, October 25, 6-7:30am PDT
Virtual Room 55 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Fostering Geographic And Linguistic Diversity In Academic Conferences<\/h5>\n

In this talk I will reflect upon my experiences as a co-chair of the 4th workshop on Mechanism Design for Social Good (MD4SG ’20) and as the current general chair of the inaugural ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO \u201921). Research within the MD4SG initiative is fundamentally interdisciplinary, inter-institutional and international. As such, the virtual nature of these events have provided a unique opportunity to increase the geographic and linguistic diversity of our participants through a variety of methods which I will share.<\/p>\n

Francisco Marmolejo; University of Oxford, United Kingdom<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Bringing STEM To Underserved Communities<\/h5>\n

Industry and academia suffer from lack of full participation in STEM fields, excluding those from traditionally marginalized groups. To help address this problem, we partnered with Code in the Schools, a non-profit in Baltimore city and STEM academy of Hollywood, a high school in Los Angeles to bring students from traditionally under-represented groups in STEM together and engage them in fun AI\/OR projects for social good. To achieve this, we held the ExplOR event in November 2020. Guided by mentors from the INFORMS community, the students worked in teams to address a range of problems in areas including public health, conservation, etc. The goal was to raise students’ interest in AI\/OR and help them build a network with mentors and fellow students.<\/p>\n

Aida Rahmattalabi, Caroline Johnston, Phebe Vayanos; University of Southern California, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Fairness, Equality, And Power In Algorithmic Decision-making<\/h5>\n

Much of the debate on the impact of algorithms is concerned with fairness, defined as the absence of discrimination for individuals with the same “merit.” We argue that leading notions of fairness suffer from three key limitations: they legitimize inequalities justified by “merit;” they are narrowly bracketed, considering only differences of treatment within the algorithm; and they consider between-group and not within-group differences. We consider two alternate perspectives: the first focuses on inequality and the causal impact of algorithms and the second on the distribution of power. We formalize these perspectives drawing on causal inference and empirical economics, and characterize when they give divergent evaluations. We present theoretical results and empirical examples, and use these insights to present a guide for algorithmic auditing.<\/p>\n

Maximilian Kasy; University of Oxford, United Kingdom<\/p> <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Developing Principles For DEI-informed Research In OR\/Analytics Through An Analysis Of Published Journal Articles<\/h5>\n

We describe a project to develop principles by which researchers in OR\/analytics may integrate ideas about diversity, equity and inclusion, racial and social justice and antiracism into research ideas that span application areas, disciplinary modes and analytic methods. These principles are derived from a mixed\u2010methods analysis of INFORMS journal publications including thematic analysis and author interviews. This project has the potential to improve the profession (how the work gets done, and the environment within which the work is done) as well as the discipline (the academic and scholarly place within which the work is situated).<\/p>\n

Michael P. Johnson1<\/sup>, Tayo Fabusuyi2<\/sup>; 1<\/sup>University of Massachusetts Boston, USA, 2<\/sup>University of Michigan, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VMA78: Relief Distribution Strategies for Vulnerable and Hard to Reach Populations <\/span>\n\n \n \n Monday, October 25, 6-7:30am PDT
Virtual Room 78 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Agent-based Simulation: Modeling the Impact of Client-choice Food Distribution at Food Pantries<\/h5>\nClient-choice food pantries are quickly becoming a vital instrument in the fight against food insecurity. This research aims to use agent-based simulation to model the impact of client-choice food distribution on client food choices at a food pantry. Using survey results, we show that such interventions result in significantly healthier food choices.\n\nBenjamin F. Morrow, Jr; North Carolina A&T State University, USA <\/div>\n<\/div>\n <\/div>\n
\n \n
\n
\n
Elicitation Of Preference Among Multiple Criteria In Food Distribution By Food Banks<\/h5>\nFood banks are nonprofit organizations that collect food donations and distribute to the food-insecure populations in their service regions. Three criteria are often considered by the food banks while determining the distribution of the donated food: equity, effectiveness, and efficiency. Models that assume predetermined sets of weights on these criteria may produce inaccurate results as the preference of the food banks over these criteria may vary. We develop a weighted multi-criteria optimization model that capture the varying preferences over the three criteria. We propose a novel algorithm that elicits the inherent preference of a food bank by analyzing its actions within a single-period and that does not require direct interaction with the decision-maker. We illustrate results using real life data from our food bank partner and discuss managerial insights.\n\nTanzid Hasnain1<\/sup>, Irem Sengul Orgut2<\/sup>, Julie Simmons Ivy1<\/sup>; 1<\/sup>North Carolina State University, USA, 2<\/sup>University of Alabama, USA <\/div>\n<\/div>\n <\/div>\n <\/div>\n
\n
\n \n
\n
\n
Drone Logistics for Uncertain Demands of Disaster-impacted Populations<\/h5>\nIn this study, we present a stochastic optimization model to address the challenges associated with timely delivery of aid packages to disaster-affected regions via a fleet of drones while considering the set of demand locations is unknown. The main problem is to locate a set of drone platforms such that with a given probability, the maximum total cost (or disutility) under all realizations of the set of demand locations is minimized. We formulate and solve a time-space drone scheduling model for a set of scenarios to build up the total disutility distribution. We also propose an algorithmic solution approach which decomposes the problem into three tractable subproblems.\n\nZabih Ghelichi1<\/sup>, Monica Gentili1<\/sup>, Pitu B. Mirchandani2<\/sup>; 1<\/sup>University of Louisville, USA, 2<\/sup>Arizona State University, USA <\/div>\n<\/div>\n <\/div>\n
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VMB09: Panel: Communicating with Stakeholders in Health <\/span>\n\n \n \n Monday, October 25, 7:45-9:15am PDT
Virtual Room 09 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n

There has been much healthcare research in our field that could make larger impact. In this panel, we invited a few researchers in our field who have done a great job in communicating with various stakeholders including industry, media, and government agencies about their research and have seen an impact from that. We are sure their stories, experiences, and maybe cautions will be very beneficial for our profession and HAS members. Together, we can increase the impact of our field through more intentional and powerful communications with the different stakeholders outside our academic world.<\/p>\n

Panelists:<\/strong>
Pinar Keskinocak; ISyE Georgia Tech, USA
Tinglong Dai; Johns Hopkins University, USA
Julie L. Swann; North Carolina State University, USA<\/p> <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VMB78: NSF Rapids Related to Covid <\/span>\n\n \n \n Monday, October 25, 7:45-9:15am PDT
Virtual Room 78 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Tracking Vaccine Distribution As An Extreme Logistics Operation<\/h5>\n

Given the unprecedented and fast-moving nature of the Covid-19 vaccine distribution operation, it is imperative to track its development and deployment. We documented the evolution of vaccine distribution in the United States through a real-time dashboard to monitor the state of the Covid 19 vaccine supply chain, including vaccination rates over time and space. Data from this effort allows us to extract lessons learned and principles for the robust design and resilient operation of future extreme logistics deployments, including health-related crises and disaster response situations.<\/p>\n

Sharika J. Hegde, Hani S. Mahmassani, Karen Smilowitz; Northwestern University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Toward Understanding Vaccine Supply Chain: Distribution And Administration Challenges<\/h5>\n

The rollout of vaccine in the COVID-19 pandemic is one of the largest efforts in public health history. We collect spatiotemporal data on vaccine allocation, shipment and distribution, administration, inventory, and policies in the US. This study performs analyses to explore the (i) number and distribution of vaccine doses and providers with respect to each state\u2019s population, (ii) distribution of vaccine doses across states and territories, pharmacies, and other vaccine awardees, (iii) time-variant shipment amount of vaccine doses from manufacturers to providers throughout the study period, (iv) cumulative percentage of vaccine doses for individuals of different age groups over time for a selection of states, (v) vaccination rate of doses by state and by race\/ethnicity to individuals of various age groups, and (vi) amount of vaccine wastage revealed at each provider.<\/p>\n

Leila Hajibabai, Ali Hajbabaie, Julie L. Swann; North Carolina State University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VMC39: Social-Ecological-Technological Systems (SETS) Resilience for Urban Sustainability in the Anthropocene <\/span>\n\n \n \n Monday, October 25, 11am-12:30pm PDT\nVirtual Room 39 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Centralization and Decentralization for Resilient Infrastructure and Complexity<\/h5>\n

Pervasive across infrastructure literature and discourse are the concepts of centralized, decentralized, and distributed systems. There does not appear to be a concerted effort to align how these concepts are used, and what different configurations mean for resilient infrastructure systems. We review framings of these concepts across infrastructure sectors, revealing polysemous framings; describe how the concepts are applied to resilient infrastructure theory, identifying conditions supporting resilience principles; and recommend a multi-dimensional framing through a network-governance perspective, where capabilities to shift between stability and instability are emphasized.<\/p>\n

Alysha Helmrich1<\/sup>, Samuel Markolf2<\/sup>, Rui Li1<\/sup>, Thomaz Carvalhaes1<\/sup>, Yeowon Kim3<\/sup>, emily Bondank1<\/sup>, Mukunth Natarajan1<\/sup>, Nasir Ahmad1<\/sup>, Mikhail Chester1<\/sup>; 1<\/sup>Arizona State University, USA, 2<\/sup>University of California Merced, USA, 3<\/sup> Carleton University, Canada<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Climate Adaptation Strategies in Cities: The Case of Air Conditioning Adoption in N.Y.C.<\/h5>\n

As climate change warms our summers, adapting to extreme heat will be increasingly important. At city-scales, however, adaptive strategies can impact many a wide range of social, environmental, and infrastructure. Here, we leverage advances in urban climate models and public data to study the socio-techno-ecological implications of full air conditioning adoption as a function of climate change, using the case of NYC. Our work highlights some of the tradeoffs involved in climate adaptation in terms of human health, peak electric demand, energy use costs, and urban heat. Finally, we show how building-scale strategies can help mitigate some of these impacts using the example of household energy burden.<\/p>\n

Luis E. Ortiz; The New School, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Leveraging Smart-meter Data To Understand How Residential Electricity Consumers Respond To Increases In Urban Warming<\/h5>\n

By 2050, approximately two-thirds of the global population is expected to live in urban regions. Urban warming, influenced by factors including climate change, urban heat islands, and population densification, is already driving large increases in the global demand for air-conditioning, but despite its importance to a city\u2019s future electricity consumption, there are still large unknowns in terms of how energy consumers respond to increases in temperature. Here we use the hourly smart meter records of nearly 200,000 electricity consuming households in the Southern California region, as well as other publicly available datasets, to characterize how households respond to extreme heat and analyze how these responses vary according to building characteristics, socio-economic status, and other climatic\/regional factors.<\/p>\n

Kelly T. Sanders; University of Southern California, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Leveraging Social-Ecological-Technological Systems Resilience Capabilities For Safe-to-fail Infrastructure And Climate Change<\/h5>\n

As the rehabilitation of infrastructure is outpaced by changes in the environment, failures become increasingly likely. Infrastructure have been designed around models of rigidity, and the deep uncertainty around climate change represents a decoupling between what our critical systems are designed to handle and how the environment is changing. If failures of infrastructure are increasingly likely then new approaches are needed to manage the consequences. Safe-to-fail approaches that call for the planning of failure responses in design to minimize damages when failures occur appear well-positioned to support resilience efforts by identifying social, ecological, and technological\/infrastructural (SETS) capabilities.<\/p>\n

Yeowon Kim1<\/sup>, Mikhail Chester2<\/sup>, Samuel Markolf3<\/sup>, Thomaz Carvalhaes2<\/sup>, Alysha Hemrich2<\/sup>, Rui Li2<\/sup>, Nasir Ahmad2<\/sup>; 1<\/sup>Carleton University, Canada, 2<\/sup>Arizona State University, USA, 3<\/sup>University of California Merced, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Towards Improved Estimates In Regional Flood Damage Through Probability Bounds Analysis<\/h5>\n

Increasing risk of floods in a changing climate is driving a greater need to assess the potential effects associated with these events. Flood risk analysis is often hampered by data quality issues, methodological challenges, ambiguous dependency among variables, and unspecified uncertainties. We illustrate a novel approach to address these difficulties through interval-type bounds on cumulative distribution functions (probability-boxes). This approach enables us to deal with the major issues that analysts face with conventional methods while differentiating between aleatoric and epistemic uncertainty and while addressing the problem of neglecting interdependencies between variates.<\/p>\n

Hiva Viseh, David N. Bristow; University of Victoria, Canada<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VMC71: Passenger Rail I <\/span>\n\n \n \n Monday, October 25, 11am-12:30pm PDT
Virtual Room 71 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Automatic Train Dispatching: A Real-life Application in the Greater Oslo Region<\/h5>\n

Serving more than a million residents, the railway network of the Greater Oslo Region is composed of several lines incident to the large Oslo central station (Oslo S). An ongoing project with Bane NOR, the Norwegian infrastructure manager aims at developing a system to dispatch trains for the entire region. A prototype of such system is currently being tested by Bane NOR dispatchers. It uses mathematical optimization and decomposition to find optimal schedules (every few seconds) based on the real-time train positions and the network status. To our knowledge, this is the largest real-life application of automatic train dispatching in Europe.<\/p>\n

Carlo Mannino, Giorgio Sartor, Andreas Nakkerud, Oddvar Kloster, Christian Schulz, Bj\u00f8rnutar Leberget, Giorgio Grani; SINTEF Digital, Norway<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Effective Pruning Strategies for the Alternative Graph Model<\/h5>\n

We present static and dynamic speed-up procedures and pruning strategies to efficiently handle hard deadline constraint in the alternative graph model. The idea is to prove that no improvement to the current best-known solution is possible if a specific sequencing\/timing decision is taken for a given partial or empty solution. Static speed-ups allow to effectively reduce the initial number of variables in a pre-processing phase. Dynamic ones take place during the solution process and can extend a partial scheduling by avoiding infeasibility areas in the search space. Computational experiments on train scheduling instances show the potential gain that can be obtained.<\/p>\n

Marcella Sama1<\/sup>, Andrea D’Ariano1<\/sup>, Dario Pacciarelli2<\/sup>; 1<\/sup>Roma Tre University, Italy, 2<\/sup>Universit\u00e0 degli Studi Roma Tre, Italy<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Train Scheduling Optimization For Commuter-metro Networks: An Improved Job-shop Formulation With Procedence Constraints<\/h5>\n

In this study, we address the train scheduling problem for commuter rail-metro systems, where the trains from commuter rail lines can go directly into metro systems to provide seamless services for passengers. In order to optimize the schedule of trains for both commuter rail lines and metro lines, we propose an improved job-shop scheduling model by taking a series of practical constraints arising from commuter-metro networks into account and develop a mixed-integer programming (MIP) model with quadratic constraints. Since these constraints involve several sets of IF-THEN logic rules, we prove that these logic constraints can be equivalently reformulated as linear inequalities, without adding new variables. Two sets of numerical experiments are implemented to verify the effectiveness (be more precise) of the proposed approaches.<\/p>\n

Jiateng Yin; Beijing Jiaotong University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Including Stochasticity In Railway Traffic Management Models<\/h5>\n

Railway traffic management determines control actions, like changing times of events or rerouting vehicles, to improve quality of operations and updating an offline timetable to delays. Those actions are taken in real-time and under uncertainty regarding the future evolution of the disturbances. How transport operators and travelers react to the decision is also subject to uncertainty. We discuss how to extend job shop models for railway traffic control to incorporate uncertainty. We present approaches going beyond the commonly accepted assumption of determinism and full certainty regarding the future operations. Uncertainty can be described by distributions or stochastic processes; stochastic optimization can solve the resulting high-dimensional stochastic control problem.<\/p>\n

Francesco Corman1<\/sup>,,1<\/sup>, Alessio Trivella1<\/sup>, Oskar Eikenbroek2<\/sup>; 1<\/sup>ETH Zurich, Switzerland, 2<\/sup>University of Twente, Netherlands<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VMD47: Green Finance <\/span>\n\n \n \n Monday, October 25, 2:45-4:15pm PDT
Virtual Room 47 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Climate And Credit Risk Prediction Of Firms By Building Benchmark Datasets And Multi-relational GCN Models<\/h5>\n

This paper investigates if one can learn about firms degree of climate risk and credit risk exposure by extracting information about their fundamentals as well as their network connections. We tackle this problem by creating multi-relational financial network (MRFN) benchmark datasets and devise novel variations of graph convolutional networks (GCN) tailored to predict the risk of firms in a semi-supervised fashion. The MRFN datasets reflective of prowess of financial analysts and market sentiment of investors are defined as two-layered networks. A multilayered nature of MRFN motivates two formulations of multi-relational GCN (MRGCN), namely a GCN via network aggregation and a GCN via supra-graph. We show that the MR-GCN models outperform the conventional uni-relational GCN models in terms of higher classification accuracy for climate and credit risk predictions.<\/p>\n

Aparna Gupta1<\/sup>, Koushik Kar1<\/sup>, Sijia Liu2<\/sup>, Sai Palepu1<\/sup>, Lucian Popa3<\/sup>, Yada Zhu3<\/sup>; 1<\/sup>Rensselaer Polytechnic Institute, USA, 2<\/sup>Michigan State University, USA, 3<\/sup>IBM, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
How Should Climate Change Uncertainty Impact Social Valuation And Policy<\/h5>\n

Mark Carney, former Governor of the Bank of England, described climate change as the “Tragedy of the Horizon.” Yet the magnitude of this “potential tragedy” and the horizon over which it will be realized are highly uncertain. Addressing the climate problem with a false sense of confidence in our understanding of the geo-scientific uncertainties and their unknown consequences for economic opportunity and social well being can be counterproductive. We explore quantitative stochastic dynamic equilibrium models enriched to include stylized specifications of carbon-climate dynamics to confront uncertainty, broadly conceived to include model ambiguity and misspecification concerns by incorporating recent advances in decision theory. Using this approach, we investigate policy questions related to the social cost of carbon and the subsidy of green technologies.<\/p>\n

Michael Barnett; Arizona State University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Housing and Mortgage Markets with Climate-Change Risk: Evidence from Wildfires in California<\/h5>\nThis paper studies the effects of climate-driven events on the housing and mortgage markets. We merge property-level data on all California wildfires from 2000 to 2018, mortgage and property characteristics, household finances, and weather. We find a significant increase in mortgage delinquency and foreclosure after a fire in the devastated areas, but these effects decrease in the size of the fire. We argue that this results from coordination externalities afforded by large fires and frictions in the insurance markets, which lead to rebuilding in the devastated areas and to increases in home sizes, house prices, income and wealth. Our results suggest that recent large losses, combined with regulatory distortions, cast doubt on the ability of insurance companies and mortgage lenders to absorb climate-related losses and assess mortgage risk.\n\nRIchard Stanton1<\/sup>, Paulo Isser1<\/sup>, Carles Vergara-Alert2<\/sup>, Nancy Wallace1<\/sup>; 1<\/sup>University of California, USA, 2<\/sup>IESE Business School, USA <\/div>\n<\/div>\n <\/div>\n
\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VMD78: Understanding Disparities and Equity in Well-being through Data-Driven Approaches <\/span>\n\n \n \n Monday, October 25, 2:45-4:15pm PDT
Virtual Room 78 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Designing Efficient And Equitable Housing Allocation Policies From Data Collected In Deployment<\/h5>\n

The VI-SPDAT is a triage tool used to assess the needs of individuals experiencing homelessness and to prioritize them for scarce housing resources. Yet the current tool does not fully leverage the available historical data, implying that resources may be allocated less efficiently and potentially result in inequitable outcomes. We propose to learn housing intervention effectiveness from data using tools from causal inference. We then incorporate these estimates into a queueing system to design a policy that allocates resources both efficiently and equitably. We evaluate our approach on data from the homeless management information system, where we show that our policies result in significant improvements in both efficiency (i.e., increase in the number of individuals that are stably housed and reduction in wait times) and equity compared to the current policy.<\/p>\n

Aida Rahmattalabi, Phebe Vayanos, Eric Rice, Kathryn Dullerud; University of Southern California, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Can\u2019t Wait: Reducing Treatment Delay For Psychiatric Patients<\/h5>\n

Hospital emergency departments (ED) are often heavily backlogged by patients in need of care but awaiting placement in an inpatient bed (IP) either at their current hospital or transfer to another facility. This is known as ED boarding and disproportionately affects patients requiring psychiatric care and to a greater extent, its subpopulation of pediatric patients. Our goal is to find novel modifications for the current system that are effective in reducing ED boarding due to lack of available IP beds, distance-related transfer restrictions, and patient-characteristic related inclusion and exclusion criteria that minimize disparities by age and geographic region.<\/p>\n

Nathan Adeyemi1<\/sup>, Nasibeh Zanjirani Farahani2<\/sup>, Amanda Graham2<\/sup>, Kalyan Pasupathy2<\/sup>, Kayse Lee Maass1<\/sup>; 1<\/sup>Northeastern University, USA, 2<\/sup>Mayo Clinic, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Using University Data to Understand Engineering Student Well-being and to Predict Dropout<\/h5>\n

This paper analyzes student information system (SIS) data collected by a university to understand how the COVID-19 pandemic has affected the well-being of undergraduate engineering students and their risk of dropping out. Various types of learning are used to characterize the dropout population and predict how influential factors of dropout may changeover time.<\/p>\n

Danika Dorris; North Carolina State University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Identifying Disparities In Access To Psychosocial Services For The Medicaid-insured Children In Georgia<\/h5>\n

The shortage of workforce providing psychosocial services is one of the most cited barriers of access to mental health treatment, resulting in long travel distances or wait times for those seeking care. However, the lack of access does not affect the population evenly. We quantify such access disparity for communities through developing an optimization model with estimated potential supply (caseload of psychosocial services) and demand (community-level psychotherapy visit counts) for Medicaid-insured children in Georgia. The statistical inference based on the model output is then used to provide policy recommendations on interventions for addressing psychosocial services\u2019 access disparities.<\/p>\n

Yujia Xie; Georgia Tech, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VTA47: Green Finance 2 <\/span>\n\n \n \n Tuesday, October 26, 6-7:30am PDT
Virtual Room 47 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Risk Management Of Energy Suppliers With Distributed Rooftop Solar Energy Resources<\/h5>\n

Increasing penetration of distributed energy resources like behind-the-meter (BTM) solar units pose several business risks to electricity aggregators\/load serving entities\/energy suppliers. This work develops a realistic profitability model to capture the cashflows of energy suppliers by quantifying the uncertainty in demand and suppression of revenue because of the increased penetration of BTM solar units. Our model captures the core co-dependencies in electricity demand, temperature and radiation at different times of the year that affect the feed-in generation from BTM solar units to the grid. We then develop a risk mitigation framework using temperature-based weather derivatives and demonstrate optimal cross hedging strategies from the energy supplier\u2019s perspective.<\/p>\n

Saptarshi Bhattacharya1<\/sup>, Aparna Gupta2<\/sup>, Koushik Kar2<\/sup>, Sai Manikant Palepu2<\/sup>; 1<\/sup>Pacific Northwest National Laboratory, USA, 2<\/sup>Rensselaer Polytechnic Institute, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Valuation of Carbon Emission Allowance Options Under an Open Trading Phase<\/h5>\n

This paper presents valuation models of emission allowance options under an emission trading scheme operating in an open trading phase, where unused allowances are banked to subsequent phases without any limit. Empirical studies are performed to show that allowance returns share similar stylized facts to those of the stock market. Three reduced-form econometrics models are introduced. Numerical illustration of the models is performed through calibration to EU ETS allowance futures option prices, where fitness of the models is assessed comparatively.<\/p>\n

Tony Wirjanto, Mingyu Fang, Ken Seng Tan; University of Waterloo, Canada<\/p>\n <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

\n
\n
\n \n
\n

\n \n VTA79: Panel: Health in Low and Middle Income Countries (LMICs) <\/span>\n\n \n \n Tuesday, October 26, 6-7:30am PDT
Virtual Room 79 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n

Healthcare delivery challenges (demand) and infrastructure (supply) vary across countries. The operating strategy, to align product, process and market segment, therefore should depend on a given LMIC’s context, rather than being the object of a health service transplanted from another context. Differences in human, physical, financial, and institutional frameworks mean a different approach may be beneficial. Panelists share their experiences about healthcare operation capacity development and operations in LMICs. They discuss observations relevant to those working in higher income countries and perspectives of innovation and engagement rather than pushing of solutions to LMICs.<\/p>\n

Panelists:<\/b>
\nRavi Anupindi; University of Michigan, USA
\nSarang Deo; Indian School of Business, India
\nKara Palamountain; Northwestern University, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n \n
\n

\n \n VWA54: Operations Research & Vulnerable Populations <\/span>\n\n \n \n Tuesday, October 26, 6-7:30am PDT
Virtual Room 54 <\/span>\n <\/h2>\n<\/div>\n\n
\n
\n \n
\n
\n
Designing Policies For Allocating Housing To Persons Experiencing Homelessness<\/h5>\n

We study the problem of allocating scarce housing resources of different types to individuals experiencing homelessness based on their observed covariates. Our goal is to leverage administrative data collected in deployment to design an online policy that maximizes mean outcomes while satisfying budget requirements. We propose a policy in which an individual receives the resource maximizing the difference between their mean treatment outcomes and the resource bid price, or roughly the opportunity cost of using a resource. Our approach has nice asymptotic guarantees and is easily interpretable. We evaluate it on synthetic and real-world Homeless Management Information System data to illustrate practical usage of our methodology.<\/p>\n

Bill Tang1<\/sup>, Phebe Vayanos1<\/sup>, Cagil Kocyigit2<\/sup>; 1<\/sup>University of Southern California, USA, 2<\/sup>University of Luxembourg, Luxembourg<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n \n
\n
\n
Analytics To Improve The United States Immigration System<\/h5>\n

The United States immigration court system is extremely backlogged with 1.3 million cases waiting to be heard. Due to large influxes of immigrants together with limited design and resources, the court system struggles to manage this growing backlog, resulting in delays that unnecessarily tax governmental and community resources. We explore the intricacies of the court system, deconstructing different elements and their respective complexity through discrete event simulation. We study possible improvements to the simulated system by adjusting its properties, such as the assignment of cases to judges, queuing discipline, hearing medium (in person, or remote), and priority queues.<\/p>\n

Geri Louise Dimas, Andrew C. Trapp, Renata Alexandra Konrad, Adam Ferrarotti; Worcester Polytechnic Institute, USA<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n

\n
\n \n
\n
\n
Reducing Vulnerability To Human Trafficking By Improving Access To Housing And Support Services<\/h5>\n

Exposure to trauma, violence, and substance use, coupled with a lack of community support services, puts runaway and homeless youth at high risk of being trafficked. Access to safe housing and supportive services such as healthcare and education is known to be an effective answer to youth\u2019s vulnerability towards exploitation. However, in most communities in the U.S. the number of youths experiencing homelessness exceeds the capacity of the housing resources available. This study involves primary data collection and an integer linear optimization model to project the collective capacity required by service providers to adequately meet the needs of these vulnerable youth in NYC.<\/p>\n

Yaren Bilge Kaya1<\/sup>, Kayse Maass1<\/sup>, Renata Alexandra Konrad2<\/sup>, Andrew C. Trapp2<\/sup>, Geri Dimas2<\/sup>; 1<\/sup>Northeastern University, USA, 2<\/sup>Worcester Polytechnic Institute, USA<\/p>\n <\/div>\n<\/div>\n <\/div>\n

\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n","protected":false},"excerpt":{"rendered":"

From the more than 5,500 abstracts submitted for the Annual Meeting program, the organizing committee has curated a collection of sessions identified as \u201cThe Committee\u2019s Choice.\u201d These sessions call attention to important challenges \u2013 climate change and policy, pandemic response, vulnerable populations, disparities and equity in well-being, urban public service, urban sustainability, and diversity, equity […]<\/p>\n","protected":false},"author":46,"featured_media":7,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-2042","page","type-page","status-publish","has-post-thumbnail","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"\nCommittee's Choice Sessions - 2021 INFORMS Annual Meeting<\/title>\n<meta name=\"description\" content=\"The 2021 INFORMS Annual Meeting Organizing Committee has designated the following presentations as the \u201cCommittee\u2019s Choice.\u201d\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Committee's Choice Sessions\" \/>\n<meta property=\"og:description\" content=\"The 2021 INFORMS Annual Meeting Organizing Committee has designated the following presentations as the \u201cCommittee\u2019s Choice.\u201d\" \/>\n<meta property=\"og:url\" content=\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/\" \/>\n<meta property=\"og:site_name\" content=\"2021 INFORMS Annual Meeting\" \/>\n<meta property=\"article:modified_time\" content=\"2021-10-12T17:00:29+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png\" \/>\n\t<meta property=\"og:image:width\" content=\"400\" \/>\n\t<meta property=\"og:image:height\" content=\"317\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/\",\"name\":\"Committee's Choice Sessions - 2021 INFORMS Annual Meeting\",\"isPartOf\":{\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png\",\"datePublished\":\"2021-10-04T16:47:43+00:00\",\"dateModified\":\"2021-10-12T17:00:29+00:00\",\"description\":\"The 2021 INFORMS Annual Meeting Organizing Committee has designated the following presentations as the \u201cCommittee\u2019s Choice.\u201d\",\"breadcrumb\":{\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#primaryimage\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png\",\"contentUrl\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png\",\"width\":400,\"height\":317,\"caption\":\"Join us for the 2021 INFORMS Annual Meeting\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Committee’s Choice Sessions\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/#website\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/\",\"name\":\"2021 INFORMS Annual Meeting\",\"description\":\"Anaheim, CA | October 24-27, 2021\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Committee's Choice Sessions - 2021 INFORMS Annual Meeting","description":"The 2021 INFORMS Annual Meeting Organizing Committee has designated the following presentations as the \u201cCommittee\u2019s Choice.\u201d","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/","og_locale":"en_US","og_type":"article","og_title":"Committee's Choice Sessions","og_description":"The 2021 INFORMS Annual Meeting Organizing Committee has designated the following presentations as the \u201cCommittee\u2019s Choice.\u201d","og_url":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/","og_site_name":"2021 INFORMS Annual Meeting","article_modified_time":"2021-10-12T17:00:29+00:00","og_image":[{"width":400,"height":317,"url":"http:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png","type":"image\/png"}],"twitter_card":"summary_large_image","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/","url":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/","name":"Committee's Choice Sessions - 2021 INFORMS Annual Meeting","isPartOf":{"@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/#website"},"primaryImageOfPage":{"@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#primaryimage"},"image":{"@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#primaryimage"},"thumbnailUrl":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png","datePublished":"2021-10-04T16:47:43+00:00","dateModified":"2021-10-12T17:00:29+00:00","description":"The 2021 INFORMS Annual Meeting Organizing Committee has designated the following presentations as the \u201cCommittee\u2019s Choice.\u201d","breadcrumb":{"@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#primaryimage","url":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png","contentUrl":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/files\/2020\/11\/2021_informs_annual_meeting_logo.png","width":400,"height":317,"caption":"Join us for the 2021 INFORMS Annual Meeting"},{"@type":"BreadcrumbList","@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/committees-choice\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/"},{"@type":"ListItem","position":2,"name":"Committee’s Choice Sessions"}]},{"@type":"WebSite","@id":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/#website","url":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/","name":"2021 INFORMS Annual Meeting","description":"Anaheim, CA | October 24-27, 2021","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"builder_content":"<p>From the more than 5,500 abstracts submitted for the Annual Meeting program, the organizing committee has curated a collection of sessions identified as \u201cThe Committee\u2019s Choice.\u201d These sessions call attention to important challenges \u2013 climate change and policy, pandemic response, vulnerable populations, disparities and equity in well-being, urban public service, urban sustainability, and diversity, equity and inclusion.<\/p> <p>Click on a link below to jump to that session's time and location, presentation titles, abstracts, and authors.<\/p> <p><strong>In-person Sessions:<\/strong><br \/> <a href=\"#tb44\">TB44: In Person - Committee's Choice<\/a><\/p> <p><strong>Virtual Sessions:<\/strong><br \/> <a href=\"#vsa55\">VSA55: Analytics for Policing and Urban Public Service Operations<\/a><br \/> <a href=\"#vsa60\">VSA60: Modeling and Measuring Insider Threat<\/a><br \/> <a href=\"#vsa72\">VSA72: Passenger Rail II<\/a><br \/> <a href=\"#vsb09\">VSB09: Distinguished Lectures: COVID - Production, Supply Chain, Analytics<\/a><br \/> <a href=\"#vsd54\">VSD54: Diversity\/PSOR\/MIF - Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice I<\/a><br \/> <a href=\"#vsd78\">VSD78: COVID-19 Response and Disparate Impact<\/a><br \/> <a href=\"#vma55\">VMA55: Diversity\/PSOR\/MIF - Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice II<\/a><br \/> <a href=\"#vma78\">VMA78: Relief Distribution Strategies for Vulnerable and Hard to Reach Populations<\/a><br \/> <a href=\"#vmb09\">VMB09: Panel: Communicating with Stakeholders in Health<\/a><br \/> <a href=\"#vmb78\">VMB78: NSF Rapids Related to Covid<\/a><br \/> <a href=\"#vmc39\">VMC39: Social-Ecological-Technological Systems (SETS) Resilience for Urban Sustainability in the Anthropocene<\/a><br \/> <a href=\"#vmc71\">VMC71: Passenger Rail I<\/a><br \/> <a href=\"#vmd47\">VMD47: Green Finance<\/a><br \/> <a href=\"#vmd78\">VMD78: Understanding Disparities and Equity in Well-being through Data-Driven Approaches<\/a><br \/> <a href=\"#vta47\">VTA47: Green Finance 2<\/a><br \/> <a href=\"#vta79\">VTA79: Panel: Health in Low and Middle Income Countries (LMICs)<\/a><br \/> <a href=\"#vwa54\">VWA54: Operations Research & Vulnerable Populations<\/a><\/p>\n<h2>In-person Sessions<\/h2>\n<h2>TB44: In Person - Committee's Choice<br\/>Tuesday, October 26, 7:45-9:15am PDT<br\/>CC - Room 304B<\/h2>\n<h5>Can\u2019t Wait: Reducing Treatment Delay For Psychiatric Patients<\/h5> <p>Hospital emergency departments (ED) are often heavily backlogged by patients in need of care but awaiting placement in an inpatient bed (IP) either at their current hospital or transfer to another facility. This is known as ED boarding and disproportionately affects patients requiring psychiatric care and to a greater extent, its subpopulation of pediatric patients. Our goal is to find novel modifications for the current system that are effective in reducing ED boarding due to lack of available IP beds, distance-related transfer restrictions, and patient-characteristic related inclusion and exclusion criteria that minimize disparities by age and geographic region.<\/p> <p>Nathan Adeyemi<sup>1<\/sup>, Nasibeh Zanjirani Farahani<sup>2<\/sup>, Amanda Graham<sup>2<\/sup>, Kalyan Pasupathy<sup>2<\/sup>, Kayse Lee Maass<sup>1<\/sup>; <sup>1<\/sup>Northeastern University, USA, <sup>2<\/sup>Mayo Clinic, USA<\/p>\n<h5>Design Of Staged Alert Systems For COVID-19<\/h5> <p>Judicious implementation and relaxation of pandemic restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. We describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in Austin, Texas through the COVID-19 pandemic, acknowledging inequities, and accounting for an exit strategy under effective vaccines.<\/p> <p>David Morton<sup>1<\/sup>, Nazlican Arslan<sup>2<\/sup>, Daniel Duque<sup>1<\/sup>, Bismark Singh<sup>3<\/sup>, Ozge Surer<sup>1<\/sup>, Haoxiang Yang<sup>4<\/sup>, Lauren Meyers<sup>5<\/sup>; <sup>1<\/sup>Northwestern University, USA, <sup>2<\/sup>northwestern university, USA, <sup>3<\/sup>Friedrich-Alexander Universitat, Germany, <sup>4<\/sup>Los Alamos National Laboratory, USA, <sup>5<\/sup>University of Texas, USA<\/p>\n<h5>Simpler (classical) And Faster (quantum) Algorithms For Gibbs Partition Functions<\/h5> <p>We consider the problem of approximating the partition function of a classical Hamiltonian using simulated annealing. This requires the computation of a cooling schedule, and the ability to estimate the mean of the Gibbs distributions at the corresponding inverse temperatures. We propose classical and quantum algorithms for these two tasks, achieving two goals: (i) we simplify the seminal work of \u0160tefankovi\u010d, Vempala and Vigoda (J. ACM, 56(3), 2009), improving their running time and almost matching that of the current classical state of the art; (ii) we quantize our new simple algorithm, improving upon the best known algorithm for computing partition functions of many problems, due to Harrow and Wei (SODA 2020). A key ingredient of our method is the paired-product estimator of Huber (Ann. Appl. Probab., 25(2),2015).<\/p> <p>Srinivasan Arunachalam<sup>1<\/sup>, Vojtech Havlicek<sup>2<\/sup>, Giacomo Nannicini<sup>2<\/sup>, Kristan Temme<sup>2<\/sup>, Pawel Wocjan<sup>2<\/sup>; <sup>1<\/sup>IBM, USA, <sup>2<\/sup>IBM T.J. Watson, USA<\/p>\n<h2>Virtual Sessions<\/h2>\n<h2>VSA39: Modeling the Environmental Impacts of Emerging Transportation Technologies and Systems<br\/>Sunday, October 24, 6-7:30am PDT<br\/>Virtual Room 39<\/h2>\n<h5>Modeling Greenhouse Gas Impacts Of Vehicle Electrification In The Us And China<\/h5> <p>As the largest two carbon emitters, the United States and China have pledged to achieve carbon neutrality by 2050 and 2060, respectively. Vehicle electrification is now widely viewed as an inevitable part of the roadmap. However, it is not yet clear to what necessary or realistic extent, vehicle electrification can contribute to the carbon neutrality goals in the two countries. In this study, we address this question by using MA3T and NEOCC to simulate adoption of electric vehicles in the two countries and considering consumer acceptance, supply decision, vehicle efficiency, battery cost, charging infrastructure, and policy forcing. The resulting greenhouse gas emissions are calculated with consideration of electricity carbon intensity over time under different grid decarbonization scenarios.<\/p> <p>Zhenhong Lin; Oak Ridge National Laboratory, USA<\/p>\n<h5>Incentives Design Optimization For Plug-in Electric Vehicles Diffusion And Target Emissions Reduction<\/h5> <p>Electric vehicles (EVs) are expected to reduce environmental externalities of transportation and contribute to the sector\u2019s decarbonization. We aim to increase EV adoption with monetary incentives (i.e., rebates and charging infrastructure investments) and achieve a target emissions reduction. A mathematical model is proposed to optimize the budget allocation of charging infrastructure deployment and EV purchase rebates over a set planning horizon. We implement a metaheuristic algorithm based on simulated annealing to solve the nonlinear programming problem. To provide comprehensive policy recommendations, we conduct sensitivity analyses to evaluate changes in the investment portfolio.<\/p> <p>Yen-Chu Wu, Eleftheria Kontou; University of Illinois at Urbana-Champaign, USA<\/p>\n<h5>Emission Reduction Potentials Of Shared Micro-mobility In Global Cities<\/h5> <p>The operation of shared micro-mobility systems includes rebalancing and charging processes, which leads to additional greenhouse gas (GHG) emissions. Shared micro-mobility systems can only reduce emissions when replacing car trips or integrating with transit. But the system operation relies on local travel patterns and the mode replacement relies on travel behavior. We will use a data-driven framework to assess GHG emissions of shared micro-mobility, which includes a life cycle assessment model for emission generation and a simulation model for emission reduction based on mode replacement. We will apply the model to global cities and compare different systems based on real-world travel patterns and riders\u2019 travel behaviors.<\/p> <p>Hao Luo; USA<\/p>\n<h5>High-impact Locations For Electric Vehicle Charging Based On Travel Behaviors And Technology Limitations<\/h5> <p>In this paper we examine travel behaviors and technological capabilities to identify high-impact locations for locating electric vehicle charging stations. We compare potential charging locations based on their ability to serve vehicle charging needs without interrupting personal travel schedules throughout the day and year. Charging locations are also compared alongside vehicle costs and battery capacities to evaluate the extent to which they provide equitable access to electric vehicles. Finally, we examine how technology improvement trajectories can be accounted for in technology and policy planning for expanding electric vehicle charging.<\/p> <p>Jessika Trancik, Wei Wei; Massachusetts Institute of Technology, USA<\/p>\n<h2>VSA55: Analytics for Policing and Urban Public Service Operations<br\/>Sunday, October 24, 6-7:30am PDT<br\/>Virtual Room 55<\/h2>\n<h5>Data-driven Optimization For Atlanta Police Zone Design<\/h5> We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload among geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incidents reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high priority 911 calls by 5.8% and the imbalance of police workload among different zones by 43%.\nShixiang Zhu; Georgia Institute of Technology, USA\n<h5>Reinforcement Learning For Fair Police Dispatch <\/h5> <p>We propose a reinforcement learning framework to determine the optimal dispatch policy for policing that takes into account proper and fair patrol coverage. Integrating results from patrol car GPS trajectory data provided by the Atlanta Police Department, we develop simulations modeling a real-world crime and policing environment. An agent treating this simulation as a Markov decision process will learn how to best dispatch individual patrollers to incidents. We will use a cross-entropy regularizer weighted by population and census demographics to prevent unfair, unbalanced policing that favors certain demographics. Maximizing the coverage of patrollers allows for faster response than greedy dispatch of the nearest car.<\/p> <p>Alexander Bukharin; Georgia Tech, USA<\/p>\n<h5>Designing A General Framework For Solving School Bus Routing Problems<\/h5> <p>School bus routing is a crucial challenge faced by many school districts. While advanced algorithms exist in the literature, they can be difficult for districts to implement and commercial software can yield poor solutions. Further, bus transportation impacts many aspects of education, so the \u201coptimal\u201d solution is often hard to define. In this work, we aim to develop a simple framework for optimizing transportation problems, starting from data analysis to optimization via visualization. We\u2019ll make use of well-developed tools such as Google OR-Tools, which can already provide near-optimal solutions. Specifically, we provide user-friendly visualizations to help decision maker understand their overall system and solutions, presenting results from a collaboration with Denver Public Schools as a case study.<\/p> <p>Min Fei, Karen Smilowitz, Sebastien Martin; Northwestern University, USA<\/p>\n<h5>Optimizing Shift Schedules And Dispatch Of Safety Patrol Officers For Denver Public Schools<\/h5> <p>Each year, the Safety Department at Denver Public Schools (DPS) manually creates patrol officer schedules to respond to calls for over 200 schools to ensure the safety of all students and staff. The Safety Department struggles to adjust schedules in response to changes such as available officers due to the manual process. To address these drawbacks, we developed optimization and simulation models to create officer shift schedules based on call demand, factor in call demand uncertainty, and estimate the performance of the generated schedules. The DPS Safety Department used one of multiple generated schedules for the 2019-2020 academic year and we able to meet their target call response times.<\/p> <p>Amanda Chu, Pinar Keskinocak, Onkar Kulkarni, Ritesh Ojha; ISyE Georgia Institute of Technology, USA<\/p>\n<h2>VSA60: Modeling and Measuring Insider Threat<br\/>Sunday, October 24, 6-7:30am PDT<br\/>Virtual Room 60<\/h2>\n<h5>Mitigating Behavior Of Poll Workers As An Insider Threat To Elections Security<\/h5> <p>Poll workers are on the first line of defense in elections security and are trusted insiders to the process. This research assesses personal computer security behaviors for poll workers using the Security Behavior Intentions Scale (SeBIS) survey and a sample of 2,213 poll workers from 13 states. An information theory model is developed to examine potential weaknesses in security behaviors and identify security practices to improve with poll workers. Outcomes from this research aid in identifying a poll worker who may pose an insider threat and mitigating unintentional consequence.<\/p> <p>Natalie M. Scala, Josh Dehlinger, Yeabsira Mezgebe; Towson University, USA<\/p>\n<h5>Neural Network Detection for Insider Threats<\/h5> <p>Insider threats present one of the largest threats to defense capabilities and readiness, but their cost is difficult to value and frequently goes undiscussed. This presentation will address several operations research applications to the prevention, detection, and mitigation of insider threats within the Army, informed by the cadet's work with the Army G-3\/5\/7 at the Pentagon.<\/p> <p>Brooke Allen; United States Military Academy, USA<\/p>\n<h5>Optimization of the DoD Insider Threat Program<\/h5> <p>Pier Bos; United States Military Academy, USA<\/p>\n<h2>VSA72: Passenger Rail II<br\/>Sunday, October 24, 6-7:30am PDT<br\/>Virtual Room 72<\/h2>\n<h5>Rolling Stock Allocation And Timetabling For Rail Transit Network With Multiple Depots<\/h5> <p>Rolling stock management and service timetabling are two challenging but very related issues in urban rail networks. The former is to allocate a certain fleet of trains to each depot, while the latter designs the arrival\/departure times of train services according to the available trains at each depot. In this study, we develop an integer linear programming (ILP) to jointly optimize the allocation of rolling stocks and train timetables for the involved lines in an urban rail network. Considering that the problem with a large time horizon involves numerous integer variables which cannot be handled efficiently, a Benders decomposition method is introduced to decompose our ILP model (whole network) into a group of small subproblems (each line). Furthermore, numeric experiments derived from the Beijing rail transit network are conducted to validate our model and algorithm.<\/p> <p>Fan Pu; Beijing Jiaotong University, China<\/p>\n<h5>Timetable Optimization For Minimizing Transfer Costs Under Through-operation<\/h5> <p>In public passenger rail systems, passengers often need to make several transfers to reach their destination, which leads to increased travel time and safety risks. In this paper, we adopt a <i>through-operation<\/i> model, which allows trains from different lines to travel on other lines, and propose a mixed integer programming model. By adjusting trains\u2019 dispatch times and headways at the stations, the model can obtain an optimal timetable, providing the minimum dwell time for on-board passengers and minimizing the transfer waiting time for all transfer passengers. The proposed model are validated by some numerical experiments based on the operational data derived from the Beijing urban rail network.<\/p> <p>Yi Zheng; Peking University, China<\/p>\n<h5>Dynamic Passenger-centric Railway Traffic Management<\/h5> <p>Railway traffic management refers to the monitoring and rescheduling of train services that are affected by disturbances. In practice, traffic management relies on traffic controllers who manually adjust train services and do not have any tools to consider passenger needs. Thus, minimizing train delays is their main goal for traffic management. However, train delays differ from passenger delays that also depend on other factors like the path choice of passengers. Hence, I will show a recent work on Passenger-Oriented Railway Traffic Management (PORTM), which focuses on the impact of decisions on passengers. The PORTM problem is formulated into a MILP model that integrates timetable rescheduling and passenger assignment. The model dynamically reschedules the timetable for disturbances that emerge over time aiming to minimize passengers' generalized travel times.<\/p> <p>Yongqiu Zhu; ETH Zurich, Switzerland<\/p>\n<h5>Trains Rescheduling Method During Multi-disturbances Under A Quasi-moving Block System<\/h5> <p>It is critical to reschedule trains timely in Chinese high-speed railway system. This paper presents a rescheduling model which can deal with multi-disturbances by retiming, reordering, rerouting and train speed adaptation under a quasi-moving block system. The alternative arcs and alternative arrival\/departure paths are designed in constraints to figure out the siding lines blockage. A custom-designed two-step method based on a commercial solver is applied to solve instances from Chinese high-speed networks quickly. The experimental cases with\/without the siding line blockage are studied respectively. The outputs demonstrates that the proposed approach can achieve a reduction of train delays by 70% compared to the solution without reordering, and the application of resetting routes can mitigate traffic tardiness and speed up rescheduling process effectively.<\/p> <p>Peijuan Xu<sup>1<\/sup>, Francesco Corman<sup>2<\/sup>; <sup>1<\/sup>Chang'an University, China, <sup>2<\/sup>ETH Zurich, Switzerland<\/p>\n<h5>Train Scheduling Optimization With Virtual Coupled Vehicles For Integrated Commuter Rail And Underground Metro Network<\/h5> <p>With the increasing of passenger demand and the saturated operations of trains, the planning process is attracting more and more attention. In this paper, we study the integration of train scheduling and virtual coupling planning under various passenger demands for integrated commuter rail and underground metro network, where the practical train operation constraints, e.g., the number of available rolling stocks, the running time of different train composition and the entering\/exiting depot operations, are considered. In order to optimize the train scheduling, we extend a job-shop scheduling model to a MINLP model. We propose an equivalent MILP formulation and develop a Benders' decomposition algorithm to improve the computational efficiency. Two sets of numerical experiments are conducted to verify the effectiveness and efficiency of our practical methods.<\/p> <p>Simin Chai; Beijing Jiaotong University, China<\/p>\n<h2>VSB09: Distinguished Lectures: COVID - Production, Supply Chain, Analytics<br\/>Sunday, October 24, 7:45-9:15am PDT Virtual Room 09<\/h2>\n<h5>COVID-19, the Global Supply Chain for Medical Products, and Operations Research<\/h5> <p>The supply chain for medical products including PPE, vaccines, testing supplies and therapeutics has faced intense public scrutiny in the last 18 months. Companies with vaccine, therapeutic, and diagnostic technologies have to make their future supply chain network design decisions against this backdrop of high policy and demand uncertainty, intense public scrutiny, and strong demands for meeting their social contract. This talk will share a few examples of how operations research models which are deeply embedded in context can help both public agencies and individual firms in making decisions related to health product purchasing, portfolio design, network design and manufacturing flexibility. It will also include findings from work on healthcare supply chain design for future pandemic preparedness.<\/p> <p>Prashant Yadav; INSEAD, France<\/p>\n<h5>Supply Chain Resiliency and the Need for Stress-Tests<\/h5> <p>As hospitalizations decrease and vaccinations increase, some see the end of the COVID-19 pandemic somewhere on the horizon. With that, many of us in the data science community are analyzing \u201clessons learned\u201d from the pandemic to better prepare and more efficiently and effectively respond to the next disaster. At the center of this discussion must be how to fix our supply chains to prevent disruptions where possible and to identify - before a disaster occurs - where vulnerabilities exist. This is the focus of my presentation.<\/p> <p>David Simchi-Levi; Massachusetts Institute of Technology, USA<\/p>\n<h2>VSD54: Diversity\/PSOR\/MIF - Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice I<br\/>Sunday, October 24, 2:45-4:15pm PDT Virtual Room 54<\/h2>\n<h5>We\u2019re Here: Interviews With LGBTQ+ Members Of The INFORMS Community<\/h5> <p>While it can be tempting to rely solely on quantitative metrics, it is also critical to humanize individuals when it comes to minority issues. This requires stories to be told, heard, and documented. The objective for this project is to use semi-structured interviews to survey, document, and report the individual stories that color and humanize data for LGBTQ+ issues. Choosing to be \u201cout\u201d in academia is a highly personal and nuanced decision, and it is one that is unique to the LGBTQ+ community. Where do ambitious students or early career faculty find an LGBTQ+ mentor in our field? What mentorship advice can be condensed and shared publicly? The aim of this work is to tackle these and other challenges with a document that is meant to be valuable for Queer and non-Queer audiences, alike. This is a work in progress sponsored by the INFORMS DEI Ambassador Program.<\/p> <p>Tyler Perini; Georgia Institute of Technology, USA<\/p>\n<h5>Challenges Faced By Black Applicants To Graduate Programs In Computing<\/h5> <p>While AI has revolutionized many sectors in society, Black people remain underrepresented within the field. In this talk, we describe the Black in AI Academic Program, a program that supports black researchers as they apply to graduate programs, navigate graduate school, and enter the postgraduate job market. We support our applicants with online information sessions, resource documents, and mentorship. We examine the impact of mentorship and the challenges that the 2019-2020 cohort in the program encountered during the application process. Overall 56% of the program participants were successfully admitted, but the lack of information, financial constraints, and unclear academic systems remain the prominent challenges that black people face in applying for AI graduate programs. We discuss the implications and offer recommendations to alleviate these challenges.<\/p> <p>Ezinne Nwankwo; University of California, Berkeley, USA<\/p>\n<h5>Diversity and Inequality in Social Networks<\/h5> <p>Online social networks often mirror inequality in real-world networks. Such disparities are often amplified by algorithms that leverage social data to provide recommendations, share information or form groups. I review explanations for algorithmic bias in social networks, addressing information diffusion, grouping, and general definitions of inequality. I use network models that reproduce inequality seen in online networks to characterize the relationship between pre-existing bias and algorithms in creating inequality, discussing different algorithmic solutions for mitigating bias. I address challenges in bridging theory and practice in studying bias and inequality.<\/p> <p>Ana-Andreea Stoica; Columbia University, USA<\/p>\n<h5>Centering Racial Equity In Research And Data Practice<\/h5> <p>Creating equitable data practices produces meaningful insights across racial groups while avoiding the common pitfalls that result in harmful data work. Legacies of discrimination can show up in data, and research practices themselves can be exploitative. Centering racial equity is not about avoiding data bias or difficult research questions but focuses on acknowledging that these challenges exist and how to create solutions for tackling them. This talk will cover some of the ways to practice racially aware data collection, analysis, and research. I will highlight high-level considerations that extend to data work with underserved communities broadly and offer resources for further development of equitable data practices.<\/p> <p>Alex Jackson; Carnegie Mellon University, USA<\/p>\n<h2>VSD78: COVID-19 Response and Disparate Impact<br\/>Sunday, October 24, 2:45-4:15pm PDT Virtual Room 78<\/h2>\n<h5>Structural Racism And Covid-19 Response: Higher Risk Of Exposure Drives Disparate Covid-19 Deaths Among Black And Hispanic\/Latinx Residents Of Illinois, USA<\/h5> <p>In 2020, Black and Latinx communities across the United States, including in Illinois, experienced disproportionately high rates of COVID-19 cases and deaths. Public health officials implemented targeted programs to increase intervention access and reduce disparities. Data on SARS-CoV-2 diagnostic tests, COVID-19 cases, and COVID-19 deaths were used to quantify the evolution of disparities in Illinois. 79.3% and 86.7% of disparities in deaths among Black and Latinx populations respectively were attributable to differences in incidence compared to White populations rather than differences in case fatality ratios. Relative lack of access to health care, crowded living conditions, and high-risk occupations are the result of structural racism, which placed Black and Latinx populations at higher risk of exposure to SARS-CoV-2 and higher COVID-19 mortality.<\/p> <p>Jaline Gerardin; Northwestern University, USA<\/p>\n<h5>Design Of Staged Alert Systems For COVID-19<\/h5> <p>Judicious implementation and relaxation of pandemic restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. We describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in Austin, Texas through the COVID-19 pandemic, acknowledging inequities, and accounting for an exit strategy under effective vaccines.<\/p> <p>David Morton<sup>1<\/sup>, Nazlican Arslan<sup>2<\/sup>, Daniel Duque<sup>1<\/sup>, Bismark Singh<sup>3<\/sup>, Ozge Surer<sup>1<\/sup>, Haoxiang Yang<sup>4<\/sup>, Lauren Meyers<sup>5<\/sup>; <sup>1<\/sup>Northwestern University, USA, <sup>2<\/sup>northwestern university, USA, <sup>3<\/sup>Friedrich-Alexander Universitat, Germany, <sup>4<\/sup>Los Alamos National Laboratory, USA, <sup>5<\/sup>University of Texas, USA<\/p>\n<h2>VMA55: Diversity\/PSOR\/MIF - Diversity, Equity and Inclusion in OR\/MS\/Analytics. Innovations in Research and Practice II<br\/>Monday, October 25, 6-7:30am PDT<br\/>Virtual Room 55<\/h2>\n<h5>Fostering Geographic And Linguistic Diversity In Academic Conferences<\/h5> <p>In this talk I will reflect upon my experiences as a co-chair of the 4th workshop on Mechanism Design for Social Good (MD4SG '20) and as the current general chair of the inaugural ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO \u201921). Research within the MD4SG initiative is fundamentally interdisciplinary, inter-institutional and international. As such, the virtual nature of these events have provided a unique opportunity to increase the geographic and linguistic diversity of our participants through a variety of methods which I will share.<\/p> <p>Francisco Marmolejo; University of Oxford, United Kingdom<\/p>\n<h5>Bringing STEM To Underserved Communities<\/h5> <p>Industry and academia suffer from lack of full participation in STEM fields, excluding those from traditionally marginalized groups. To help address this problem, we partnered with Code in the Schools, a non-profit in Baltimore city and STEM academy of Hollywood, a high school in Los Angeles to bring students from traditionally under-represented groups in STEM together and engage them in fun AI\/OR projects for social good. To achieve this, we held the ExplOR event in November 2020. Guided by mentors from the INFORMS community, the students worked in teams to address a range of problems in areas including public health, conservation, etc. The goal was to raise students' interest in AI\/OR and help them build a network with mentors and fellow students.<\/p> <p>Aida Rahmattalabi, Caroline Johnston, Phebe Vayanos; University of Southern California, USA<\/p>\n<h5>Fairness, Equality, And Power In Algorithmic Decision-making<\/h5> <p>Much of the debate on the impact of algorithms is concerned with fairness, defined as the absence of discrimination for individuals with the same \"merit.\" We argue that leading notions of fairness suffer from three key limitations: they legitimize inequalities justified by \"merit;\" they are narrowly bracketed, considering only differences of treatment within the algorithm; and they consider between-group and not within-group differences. We consider two alternate perspectives: the first focuses on inequality and the causal impact of algorithms and the second on the distribution of power. We formalize these perspectives drawing on causal inference and empirical economics, and characterize when they give divergent evaluations. We present theoretical results and empirical examples, and use these insights to present a guide for algorithmic auditing.<\/p> <p>Maximilian Kasy; University of Oxford, United Kingdom<\/p>\n<h5>Developing Principles For DEI-informed Research In OR\/Analytics Through An Analysis Of Published Journal Articles<\/h5> <p>We describe a project to develop principles by which researchers in OR\/analytics may integrate ideas about diversity, equity and inclusion, racial and social justice and antiracism into research ideas that span application areas, disciplinary modes and analytic methods. These principles are derived from a mixed\u2010methods analysis of INFORMS journal publications including thematic analysis and author interviews. This project has the potential to improve the profession (how the work gets done, and the environment within which the work is done) as well as the discipline (the academic and scholarly place within which the work is situated).<\/p> <p>Michael P. Johnson<sup>1<\/sup>, Tayo Fabusuyi<sup>2<\/sup>; <sup>1<\/sup>University of Massachusetts Boston, USA, <sup>2<\/sup>University of Michigan, USA<\/p>\n<h2>VMA78: Relief Distribution Strategies for Vulnerable and Hard to Reach Populations<br\/>Monday, October 25, 6-7:30am PDT<br\/>Virtual Room 78<\/h2>\n<h5>Agent-based Simulation: Modeling the Impact of Client-choice Food Distribution at Food Pantries<\/h5> Client-choice food pantries are quickly becoming a vital instrument in the fight against food insecurity. This research aims to use agent-based simulation to model the impact of client-choice food distribution on client food choices at a food pantry. Using survey results, we show that such interventions result in significantly healthier food choices.\nBenjamin F. Morrow, Jr; North Carolina A&T State University, USA\n<h5>Elicitation Of Preference Among Multiple Criteria In Food Distribution By Food Banks<\/h5> Food banks are nonprofit organizations that collect food donations and distribute to the food-insecure populations in their service regions. Three criteria are often considered by the food banks while determining the distribution of the donated food: equity, effectiveness, and efficiency. Models that assume predetermined sets of weights on these criteria may produce inaccurate results as the preference of the food banks over these criteria may vary. We develop a weighted multi-criteria optimization model that capture the varying preferences over the three criteria. We propose a novel algorithm that elicits the inherent preference of a food bank by analyzing its actions within a single-period and that does not require direct interaction with the decision-maker. We illustrate results using real life data from our food bank partner and discuss managerial insights.\nTanzid Hasnain<sup>1<\/sup>, Irem Sengul Orgut<sup>2<\/sup>, Julie Simmons Ivy<sup>1<\/sup>; <sup>1<\/sup>North Carolina State University, USA, <sup>2<\/sup>University of Alabama, USA\n<h5>Drone Logistics for Uncertain Demands of Disaster-impacted Populations<\/h5> In this study, we present a stochastic optimization model to address the challenges associated with timely delivery of aid packages to disaster-affected regions via a fleet of drones while considering the set of demand locations is unknown. The main problem is to locate a set of drone platforms such that with a given probability, the maximum total cost (or disutility) under all realizations of the set of demand locations is minimized. We formulate and solve a time-space drone scheduling model for a set of scenarios to build up the total disutility distribution. We also propose an algorithmic solution approach which decomposes the problem into three tractable subproblems.\nZabih Ghelichi<sup>1<\/sup>, Monica Gentili<sup>1<\/sup>, Pitu B. Mirchandani<sup>2<\/sup>; <sup>1<\/sup>University of Louisville, USA, <sup>2<\/sup>Arizona State University, USA\n<h2>VMB09: Panel: Communicating with Stakeholders in Health<br\/>Monday, October 25, 7:45-9:15am PDT<br\/>Virtual Room 09<\/h2>\n<p>There has been much healthcare research in our field that could make larger impact. In this panel, we invited a few researchers in our field who have done a great job in communicating with various stakeholders including industry, media, and government agencies about their research and have seen an impact from that. We are sure their stories, experiences, and maybe cautions will be very beneficial for our profession and HAS members. Together, we can increase the impact of our field through more intentional and powerful communications with the different stakeholders outside our academic world.<\/p> <p><strong>Panelists:<\/strong><br>Pinar Keskinocak; ISyE Georgia Tech, USA<br>Tinglong Dai; Johns Hopkins University, USA<br>Julie L. Swann; North Carolina State University, USA<\/p>\n<h2>VMB78: NSF Rapids Related to Covid<br\/>Monday, October 25, 7:45-9:15am PDT<br\/>Virtual Room 78<\/h2>\n<h5>Tracking Vaccine Distribution As An Extreme Logistics Operation<\/h5> <p>Given the unprecedented and fast-moving nature of the Covid-19 vaccine distribution operation, it is imperative to track its development and deployment. We documented the evolution of vaccine distribution in the United States through a real-time dashboard to monitor the state of the Covid 19 vaccine supply chain, including vaccination rates over time and space. Data from this effort allows us to extract lessons learned and principles for the robust design and resilient operation of future extreme logistics deployments, including health-related crises and disaster response situations.<\/p> <p>Sharika J. Hegde, Hani S. Mahmassani, Karen Smilowitz; Northwestern University, USA<\/p>\n<h5>Toward Understanding Vaccine Supply Chain: Distribution And Administration Challenges<\/h5> <p>The rollout of vaccine in the COVID-19 pandemic is one of the largest efforts in public health history. We collect spatiotemporal data on vaccine allocation, shipment and distribution, administration, inventory, and policies in the US. This study performs analyses to explore the (i) number and distribution of vaccine doses and providers with respect to each state\u2019s population, (ii) distribution of vaccine doses across states and territories, pharmacies, and other vaccine awardees, (iii) time-variant shipment amount of vaccine doses from manufacturers to providers throughout the study period, (iv) cumulative percentage of vaccine doses for individuals of different age groups over time for a selection of states, (v) vaccination rate of doses by state and by race\/ethnicity to individuals of various age groups, and (vi) amount of vaccine wastage revealed at each provider.<\/p> <p>Leila Hajibabai, Ali Hajbabaie, Julie L. Swann; North Carolina State University, USA<\/p>\n<h2>VMC39: Social-Ecological-Technological Systems (SETS) Resilience for Urban Sustainability in the Anthropocene<br\/>Monday, October 25, 11am-12:30pm PDT Virtual Room 39<\/h2>\n<h5>Centralization and Decentralization for Resilient Infrastructure and Complexity<\/h5> <p>Pervasive across infrastructure literature and discourse are the concepts of centralized, decentralized, and distributed systems. There does not appear to be a concerted effort to align how these concepts are used, and what different configurations mean for resilient infrastructure systems. We review framings of these concepts across infrastructure sectors, revealing polysemous framings; describe how the concepts are applied to resilient infrastructure theory, identifying conditions supporting resilience principles; and recommend a multi-dimensional framing through a network-governance perspective, where capabilities to shift between stability and instability are emphasized.<\/p> <p>Alysha Helmrich<sup>1<\/sup>, Samuel Markolf<sup>2<\/sup>, Rui Li<sup>1<\/sup>, Thomaz Carvalhaes<sup>1<\/sup>, Yeowon Kim<sup>3<\/sup>, emily Bondank<sup>1<\/sup>, Mukunth Natarajan<sup>1<\/sup>, Nasir Ahmad<sup>1<\/sup>, Mikhail Chester<sup>1<\/sup>; <sup>1<\/sup>Arizona State University, USA, <sup>2<\/sup>University of California Merced, USA, <sup>3<\/sup> Carleton University, Canada<\/p>\n<h5>Climate Adaptation Strategies in Cities: The Case of Air Conditioning Adoption in N.Y.C.<\/h5> <p>As climate change warms our summers, adapting to extreme heat will be increasingly important. At city-scales, however, adaptive strategies can impact many a wide range of social, environmental, and infrastructure. Here, we leverage advances in urban climate models and public data to study the socio-techno-ecological implications of full air conditioning adoption as a function of climate change, using the case of NYC. Our work highlights some of the tradeoffs involved in climate adaptation in terms of human health, peak electric demand, energy use costs, and urban heat. Finally, we show how building-scale strategies can help mitigate some of these impacts using the example of household energy burden.<\/p> <p>Luis E. Ortiz; The New School, USA<\/p>\n<h5>Leveraging Smart-meter Data To Understand How Residential Electricity Consumers Respond To Increases In Urban Warming<\/h5> <p>By 2050, approximately two-thirds of the global population is expected to live in urban regions. Urban warming, influenced by factors including climate change, urban heat islands, and population densification, is already driving large increases in the global demand for air-conditioning, but despite its importance to a city\u2019s future electricity consumption, there are still large unknowns in terms of how energy consumers respond to increases in temperature. Here we use the hourly smart meter records of nearly 200,000 electricity consuming households in the Southern California region, as well as other publicly available datasets, to characterize how households respond to extreme heat and analyze how these responses vary according to building characteristics, socio-economic status, and other climatic\/regional factors.<\/p> <p>Kelly T. Sanders; University of Southern California, USA<\/p>\n<h5>Leveraging Social-Ecological-Technological Systems Resilience Capabilities For Safe-to-fail Infrastructure And Climate Change<\/h5> <p>As the rehabilitation of infrastructure is outpaced by changes in the environment, failures become increasingly likely. Infrastructure have been designed around models of rigidity, and the deep uncertainty around climate change represents a decoupling between what our critical systems are designed to handle and how the environment is changing. If failures of infrastructure are increasingly likely then new approaches are needed to manage the consequences. Safe-to-fail approaches that call for the planning of failure responses in design to minimize damages when failures occur appear well-positioned to support resilience efforts by identifying social, ecological, and technological\/infrastructural (SETS) capabilities.<\/p> <p>Yeowon Kim<sup>1<\/sup>, Mikhail Chester<sup>2<\/sup>, Samuel Markolf<sup>3<\/sup>, Thomaz Carvalhaes<sup>2<\/sup>, Alysha Hemrich<sup>2<\/sup>, Rui Li<sup>2<\/sup>, Nasir Ahmad<sup>2<\/sup>; <sup>1<\/sup>Carleton University, Canada, <sup>2<\/sup>Arizona State University, USA, <sup>3<\/sup>University of California Merced, USA<\/p>\n<h5>Towards Improved Estimates In Regional Flood Damage Through Probability Bounds Analysis<\/h5> <p>Increasing risk of floods in a changing climate is driving a greater need to assess the potential effects associated with these events. Flood risk analysis is often hampered by data quality issues, methodological challenges, ambiguous dependency among variables, and unspecified uncertainties. We illustrate a novel approach to address these difficulties through interval-type bounds on cumulative distribution functions (probability-boxes). This approach enables us to deal with the major issues that analysts face with conventional methods while differentiating between aleatoric and epistemic uncertainty and while addressing the problem of neglecting interdependencies between variates.<\/p> <p>Hiva Viseh, David N. Bristow; University of Victoria, Canada<\/p>\n<h2>VMC71: Passenger Rail I<br\/>Monday, October 25, 11am-12:30pm PDT<br\/>Virtual Room 71<\/h2>\n<h5>Automatic Train Dispatching: A Real-life Application in the Greater Oslo Region<\/h5> <p>Serving more than a million residents, the railway network of the Greater Oslo Region is composed of several lines incident to the large Oslo central station (Oslo S). An ongoing project with Bane NOR, the Norwegian infrastructure manager aims at developing a system to dispatch trains for the entire region. A prototype of such system is currently being tested by Bane NOR dispatchers. It uses mathematical optimization and decomposition to find optimal schedules (every few seconds) based on the real-time train positions and the network status. To our knowledge, this is the largest real-life application of automatic train dispatching in Europe.<\/p> <p>Carlo Mannino, Giorgio Sartor, Andreas Nakkerud, Oddvar Kloster, Christian Schulz, Bj\u00f8rnutar Leberget, Giorgio Grani; SINTEF Digital, Norway<\/p>\n<h5>Effective Pruning Strategies for the Alternative Graph Model<\/h5> <p>We present static and dynamic speed-up procedures and pruning strategies to efficiently handle hard deadline constraint in the alternative graph model. The idea is to prove that no improvement to the current best-known solution is possible if a specific sequencing\/timing decision is taken for a given partial or empty solution. Static speed-ups allow to effectively reduce the initial number of variables in a pre-processing phase. Dynamic ones take place during the solution process and can extend a partial scheduling by avoiding infeasibility areas in the search space. Computational experiments on train scheduling instances show the potential gain that can be obtained.<\/p> <p>Marcella Sama<sup>1<\/sup>, Andrea D'Ariano<sup>1<\/sup>, Dario Pacciarelli<sup>2<\/sup>; <sup>1<\/sup>Roma Tre University, Italy, <sup>2<\/sup>Universit\u00e0 degli Studi Roma Tre, Italy<\/p>\n<h5>Train Scheduling Optimization For Commuter-metro Networks: An Improved Job-shop Formulation With Procedence Constraints<\/h5> <p>In this study, we address the train scheduling problem for commuter rail-metro systems, where the trains from commuter rail lines can go directly into metro systems to provide seamless services for passengers. In order to optimize the schedule of trains for both commuter rail lines and metro lines, we propose an improved job-shop scheduling model by taking a series of practical constraints arising from commuter-metro networks into account and develop a mixed-integer programming (MIP) model with quadratic constraints. Since these constraints involve several sets of IF-THEN logic rules, we prove that these logic constraints can be equivalently reformulated as linear inequalities, without adding new variables. Two sets of numerical experiments are implemented to verify the effectiveness (be more precise) of the proposed approaches.<\/p> <p>Jiateng Yin; Beijing Jiaotong University, USA<\/p>\n<h5>Including Stochasticity In Railway Traffic Management Models<\/h5> <p>Railway traffic management determines control actions, like changing times of events or rerouting vehicles, to improve quality of operations and updating an offline timetable to delays. Those actions are taken in real-time and under uncertainty regarding the future evolution of the disturbances. How transport operators and travelers react to the decision is also subject to uncertainty. We discuss how to extend job shop models for railway traffic control to incorporate uncertainty. We present approaches going beyond the commonly accepted assumption of determinism and full certainty regarding the future operations. Uncertainty can be described by distributions or stochastic processes; stochastic optimization can solve the resulting high-dimensional stochastic control problem.<\/p> <p>Francesco Corman<sup>1<\/sup>,<sup>,1<\/sup>, Alessio Trivella<sup>1<\/sup>, Oskar Eikenbroek<sup>2<\/sup>; <sup>1<\/sup>ETH Zurich, Switzerland, <sup>2<\/sup>University of Twente, Netherlands<\/p>\n<h2>VMD47: Green Finance<br\/>Monday, October 25, 2:45-4:15pm PDT<br\/>Virtual Room 47<\/h2>\n<h5>Climate And Credit Risk Prediction Of Firms By Building Benchmark Datasets And Multi-relational GCN Models<\/h5> <p>This paper investigates if one can learn about firms degree of climate risk and credit risk exposure by extracting information about their fundamentals as well as their network connections. We tackle this problem by creating multi-relational financial network (MRFN) benchmark datasets and devise novel variations of graph convolutional networks (GCN) tailored to predict the risk of firms in a semi-supervised fashion. The MRFN datasets reflective of prowess of financial analysts and market sentiment of investors are defined as two-layered networks. A multilayered nature of MRFN motivates two formulations of multi-relational GCN (MRGCN), namely a GCN via network aggregation and a GCN via supra-graph. We show that the MR-GCN models outperform the conventional uni-relational GCN models in terms of higher classification accuracy for climate and credit risk predictions.<\/p> <p>Aparna Gupta<sup>1<\/sup>, Koushik Kar<sup>1<\/sup>, Sijia Liu<sup>2<\/sup>, Sai Palepu<sup>1<\/sup>, Lucian Popa<sup>3<\/sup>, Yada Zhu<sup>3<\/sup>; <sup>1<\/sup>Rensselaer Polytechnic Institute, USA, <sup>2<\/sup>Michigan State University, USA, <sup>3<\/sup>IBM, USA<\/p>\n<h5>How Should Climate Change Uncertainty Impact Social Valuation And Policy<\/h5> <p>Mark Carney, former Governor of the Bank of England, described climate change as the \"Tragedy of the Horizon.\" Yet the magnitude of this \"potential tragedy\" and the horizon over which it will be realized are highly uncertain. Addressing the climate problem with a false sense of confidence in our understanding of the geo-scientific uncertainties and their unknown consequences for economic opportunity and social well being can be counterproductive. We explore quantitative stochastic dynamic equilibrium models enriched to include stylized specifications of carbon-climate dynamics to confront uncertainty, broadly conceived to include model ambiguity and misspecification concerns by incorporating recent advances in decision theory. Using this approach, we investigate policy questions related to the social cost of carbon and the subsidy of green technologies.<\/p> <p>Michael Barnett; Arizona State University, USA<\/p>\n<h5>Housing and Mortgage Markets with Climate-Change Risk: Evidence from Wildfires in California<\/h5> This paper studies the effects of climate-driven events on the housing and mortgage markets. We merge property-level data on all California wildfires from 2000 to 2018, mortgage and property characteristics, household finances, and weather. We find a significant increase in mortgage delinquency and foreclosure after a fire in the devastated areas, but these effects decrease in the size of the fire. We argue that this results from coordination externalities afforded by large fires and frictions in the insurance markets, which lead to rebuilding in the devastated areas and to increases in home sizes, house prices, income and wealth. Our results suggest that recent large losses, combined with regulatory distortions, cast doubt on the ability of insurance companies and mortgage lenders to absorb climate-related losses and assess mortgage risk.\nRIchard Stanton<sup>1<\/sup>, Paulo Isser<sup>1<\/sup>, Carles Vergara-Alert<sup>2<\/sup>, Nancy Wallace<sup>1<\/sup>; <sup>1<\/sup>University of California, USA, <sup>2<\/sup>IESE Business School, USA\n<h2>VMD78: Understanding Disparities and Equity in Well-being through Data-Driven Approaches<br\/>Monday, October 25, 2:45-4:15pm PDT<br\/>Virtual Room 78<\/h2>\n<h5>Designing Efficient And Equitable Housing Allocation Policies From Data Collected In Deployment<\/h5> <p>The VI-SPDAT is a triage tool used to assess the needs of individuals experiencing homelessness and to prioritize them for scarce housing resources. Yet the current tool does not fully leverage the available historical data, implying that resources may be allocated less efficiently and potentially result in inequitable outcomes. We propose to learn housing intervention effectiveness from data using tools from causal inference. We then incorporate these estimates into a queueing system to design a policy that allocates resources both efficiently and equitably. We evaluate our approach on data from the homeless management information system, where we show that our policies result in significant improvements in both efficiency (i.e., increase in the number of individuals that are stably housed and reduction in wait times) and equity compared to the current policy.<\/p> <p>Aida Rahmattalabi, Phebe Vayanos, Eric Rice, Kathryn Dullerud; University of Southern California, USA<\/p>\n<h5>Can\u2019t Wait: Reducing Treatment Delay For Psychiatric Patients<\/h5> <p>Hospital emergency departments (ED) are often heavily backlogged by patients in need of care but awaiting placement in an inpatient bed (IP) either at their current hospital or transfer to another facility. This is known as ED boarding and disproportionately affects patients requiring psychiatric care and to a greater extent, its subpopulation of pediatric patients. Our goal is to find novel modifications for the current system that are effective in reducing ED boarding due to lack of available IP beds, distance-related transfer restrictions, and patient-characteristic related inclusion and exclusion criteria that minimize disparities by age and geographic region.<\/p> <p>Nathan Adeyemi<sup>1<\/sup>, Nasibeh Zanjirani Farahani<sup>2<\/sup>, Amanda Graham<sup>2<\/sup>, Kalyan Pasupathy<sup>2<\/sup>, Kayse Lee Maass<sup>1<\/sup>; <sup>1<\/sup>Northeastern University, USA, <sup>2<\/sup>Mayo Clinic, USA<\/p>\n<h5>Using University Data to Understand Engineering Student Well-being and to Predict Dropout<\/h5> <p>This paper analyzes student information system (SIS) data collected by a university to understand how the COVID-19 pandemic has affected the well-being of undergraduate engineering students and their risk of dropping out. Various types of learning are used to characterize the dropout population and predict how influential factors of dropout may changeover time.<\/p> <p>Danika Dorris; North Carolina State University, USA<\/p>\n<h5>Identifying Disparities In Access To Psychosocial Services For The Medicaid-insured Children In Georgia<\/h5> <p>The shortage of workforce providing psychosocial services is one of the most cited barriers of access to mental health treatment, resulting in long travel distances or wait times for those seeking care. However, the lack of access does not affect the population evenly. We quantify such access disparity for communities through developing an optimization model with estimated potential supply (caseload of psychosocial services) and demand (community-level psychotherapy visit counts) for Medicaid-insured children in Georgia. The statistical inference based on the model output is then used to provide policy recommendations on interventions for addressing psychosocial services\u2019 access disparities.<\/p> <p>Yujia Xie; Georgia Tech, USA<\/p>\n<h2>VTA47: Green Finance 2<br\/>Tuesday, October 26, 6-7:30am PDT<br\/>Virtual Room 47<\/h2>\n<h5>Risk Management Of Energy Suppliers With Distributed Rooftop Solar Energy Resources<\/h5> <p>Increasing penetration of distributed energy resources like behind-the-meter (BTM) solar units pose several business risks to electricity aggregators\/load serving entities\/energy suppliers. This work develops a realistic profitability model to capture the cashflows of energy suppliers by quantifying the uncertainty in demand and suppression of revenue because of the increased penetration of BTM solar units. Our model captures the core co-dependencies in electricity demand, temperature and radiation at different times of the year that affect the feed-in generation from BTM solar units to the grid. We then develop a risk mitigation framework using temperature-based weather derivatives and demonstrate optimal cross hedging strategies from the energy supplier\u2019s perspective.<\/p> <p>Saptarshi Bhattacharya<sup>1<\/sup>, Aparna Gupta<sup>2<\/sup>, Koushik Kar<sup>2<\/sup>, Sai Manikant Palepu<sup>2<\/sup>; <sup>1<\/sup>Pacific Northwest National Laboratory, USA, <sup>2<\/sup>Rensselaer Polytechnic Institute, USA<\/p>\n<h5>Valuation of Carbon Emission Allowance Options Under an Open Trading Phase<\/h5> <p>This paper presents valuation models of emission allowance options under an emission trading scheme operating in an open trading phase, where unused allowances are banked to subsequent phases without any limit. Empirical studies are performed to show that allowance returns share similar stylized facts to those of the stock market. Three reduced-form econometrics models are introduced. Numerical illustration of the models is performed through calibration to EU ETS allowance futures option prices, where fitness of the models is assessed comparatively.<\/p> <p>Tony Wirjanto, Mingyu Fang, Ken Seng Tan; University of Waterloo, Canada<\/p>\n<h2>VTA79: Panel: Health in Low and Middle Income Countries (LMICs)<br\/>Tuesday, October 26, 6-7:30am PDT<br\/>Virtual Room 79<\/h2>\n<p>Healthcare delivery challenges (demand) and infrastructure (supply) vary across countries. The operating strategy, to align product, process and market segment, therefore should depend on a given LMIC's context, rather than being the object of a health service transplanted from another context. Differences in human, physical, financial, and institutional frameworks mean a different approach may be beneficial. Panelists share their experiences about healthcare operation capacity development and operations in LMICs. They discuss observations relevant to those working in higher income countries and perspectives of innovation and engagement rather than pushing of solutions to LMICs.<\/p> <p><b>Panelists:<\/b><br \/> Ravi Anupindi; University of Michigan, USA<br \/> Sarang Deo; Indian School of Business, India<br \/> Kara Palamountain; Northwestern University, USA<\/p>\n<h2>VWA54: Operations Research & Vulnerable Populations<br\/>Tuesday, October 26, 6-7:30am PDT<br\/>Virtual Room 54<\/h2>\n<h5>Designing Policies For Allocating Housing To Persons Experiencing Homelessness<\/h5> <p>We study the problem of allocating scarce housing resources of different types to individuals experiencing homelessness based on their observed covariates. Our goal is to leverage administrative data collected in deployment to design an online policy that maximizes mean outcomes while satisfying budget requirements. We propose a policy in which an individual receives the resource maximizing the difference between their mean treatment outcomes and the resource bid price, or roughly the opportunity cost of using a resource. Our approach has nice asymptotic guarantees and is easily interpretable. We evaluate it on synthetic and real-world Homeless Management Information System data to illustrate practical usage of our methodology.<\/p> <p>Bill Tang<sup>1<\/sup>, Phebe Vayanos<sup>1<\/sup>, Cagil Kocyigit<sup>2<\/sup>; <sup>1<\/sup>University of Southern California, USA, <sup>2<\/sup>University of Luxembourg, Luxembourg<\/p>\n<h5>Analytics To Improve The United States Immigration System<\/h5> <p>The United States immigration court system is extremely backlogged with 1.3 million cases waiting to be heard. Due to large influxes of immigrants together with limited design and resources, the court system struggles to manage this growing backlog, resulting in delays that unnecessarily tax governmental and community resources. We explore the intricacies of the court system, deconstructing different elements and their respective complexity through discrete event simulation. We study possible improvements to the simulated system by adjusting its properties, such as the assignment of cases to judges, queuing discipline, hearing medium (in person, or remote), and priority queues.<\/p> <p>Geri Louise Dimas, Andrew C. Trapp, Renata Alexandra Konrad, Adam Ferrarotti; Worcester Polytechnic Institute, USA<\/p>\n<h5>Reducing Vulnerability To Human Trafficking By Improving Access To Housing And Support Services<\/h5> <p>Exposure to trauma, violence, and substance use, coupled with a lack of community support services, puts runaway and homeless youth at high risk of being trafficked. Access to safe housing and supportive services such as healthcare and education is known to be an effective answer to youth\u2019s vulnerability towards exploitation. However, in most communities in the U.S. the number of youths experiencing homelessness exceeds the capacity of the housing resources available. This study involves primary data collection and an integer linear optimization model to project the collective capacity required by service providers to adequately meet the needs of these vulnerable youth in NYC.<\/p> <p>Yaren Bilge Kaya<sup>1<\/sup>, Kayse Maass<sup>1<\/sup>, Renata Alexandra Konrad<sup>2<\/sup>, Andrew C. Trapp<sup>2<\/sup>, Geri Dimas<sup>2<\/sup>; <sup>1<\/sup>Northeastern University, USA, <sup>2<\/sup>Worcester Polytechnic Institute, USA<\/p>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/pages\/2042","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/users\/46"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/comments?post=2042"}],"version-history":[{"count":22,"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/pages\/2042\/revisions"}],"predecessor-version":[{"id":2117,"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/pages\/2042\/revisions\/2117"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/media\/7"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/anaheim2021\/wp-json\/wp\/v2\/media?parent=2042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}