The 2017 INFORMS Prize winners will present on Monday, April 16 in the INFORMS Prizes and Special Sessions track.
9:10am-10am U.S. Air Force
1:50pm-2:40pm The Walt Disney Company
This prize is awarded annually to the company that effectively integrates analytics into organizational decision-making, and has repeatedly applied ORMS principles in pioneering, novel and lasting ways. The 2017 prize winners, Disney and U.S. Air Force, will describe their innovative O.R. work in a regular conference session. The 2018 winner will be recognized at the Edelman Gala on Monday evening.
Previous winners include GM, Chevron, Memorial Sloan-Kettering Cancer Center, Sasol, Jeppesen, Intel, General Electric Global Research Center, Schneider National, Air Products and Chemicals, Procter & Gamble, UPS and other leading companies.
UPS George D. Smith Prize
The George D. Smith Prize is aimed at strengthening ties between academia and industry by rewarding institutions of higher education for effective and innovative preparation of students to be good practitioners of operations research. The Prize is generously underwritten by UPS. Awarded for the first time in 2012, past winners are Carnegie Mellon University, H. John Heinz III College, Sauder School of Business, University of British Columbia – Center for Operations Excellence, MIT Leaders for Global Operations, Naval Postgraduate School, and Tauber Institute for Global Operations at University of Michigan.
The Smith Prize winner will be announced at the Edelman Gala on Monday, April 16. The 2018 winner will give their presentation on Tuesday, April 17 from 4:40pm-5:30pm in the INFORMS Prizes & Special Sessions Track.
The 2018 finalists are:
- School of Operations Research and Information Engineering, MEng Programs, Cornell University
- Haslam College of Business MSBA, University of Tennessee
Daniel H. Wagner Prize for Excellence in Operations Research Practice
Sponsored by the Practice Section of INFORMS
The 2017 Wagner Prize Reprise will take place on Monday, April 16 in the INFORMS Prizes & Special Sessions Track.
This prize emphasizes the quality and coherence of the analysis used in practice. Dr. Wagner strove for strong mathematics applied to practical problems, supported by clear and intelligible writing. The Wagner Prize recognizes those principles by emphasizing good writing, strong analytical content and verifiable practice successes. The competition is held and the winner is announced at the INFORMS Annual Meeting in the fall.
The 2017 winner, Lehigh and the Pennsylvania Department of Corrections, will reprise their presentation, “The Inmate Assignment and Scheduling Problem and its Application in the PA Department of Corrections,” at this conference.
The inmate assignment project, in close collaboration with the PA Dept. of Corrections, took five years from start to successful implementation. Our novel Inmate Assignment Decision Support System (IADSS) is designed with the main goal of simultaneously, and system-wide optimally, assigning the inmates to the correctional institutions. IADSS includes a new hierarchical multi-objective MILO model, which accurately describes the inmate assignment problem. This is the first time that OR methodologies have been used to optimize the operations, and built into the routine business practice, of a correctional system, thus it opens a rich and untouched area for the application of O.R.
Past awardees include practitioners and researchers from The Forestry Research Institute of Sweden, CDC, Ford, U.S. Coast Guard, Intel, IBM T. J. Watson Research, Schneider National, Boston University, University of Florida, and others.
Innovative Applications in Analytics Award
Sponsored by Caterpillar and the INFORMS Analytics Society
The IAAA Finalists will present on Tuesday, April 17 in the INFORMS Prizes & Special Sessions Track. The winner will be announced during the networking lunch on Tuesday.
The purpose this award is to recognize the creative and unique application of a combination of analytical techniques in a new area. The prize promotes the awareness and value of the creative combination of analytics techniques in unusual applications to provide insights and business value. All finalists will be presenting their projects at the 2018 INFORMS Analytics Conference in Baltimore, MD in April. The Analytics Section leadership would like to cordially thank all the members of the judging committee for their hard work in selecting these finalists.
The 2018 Innovative Applications in Analytics Awards Finalists are:
- BNSF: Automatic Train Identification: Multidisciplinary Approach to Improve Safety and Efficiency
BNSF Railway has invested significant capital on various track-side detectors to monitor the condition of critical mechanical parts including wheels and bearings to enable proactive maintenance of assets. Among others, we have 1200 Hot Box Detectors (HBD’s) which measure wheel bearing temperature as trains pass by and generate 25,000 messages daily. Many HBD’s are in locations which lack Automatic Equipment Identification (AEI) devices. This presents the unique challenge of accurately matching HBD measurements with the corresponding train, car, and axle. The Operations Research team at BNSF developed a suite of descriptive, predictive, and prescriptive analytics tools that significantly improved train matching efficiency and accuracy. Compared to the legacy system, the new system has improved train matching rates from 75% to 98% and reduced processing time by 70 seconds. Additionally, these algorithms directly eliminated the need to install AEI devices (approximately $150 million) near HBD’s across our network.
- IBM: Analytics to Reduce Costs and Improve Quality in Wastewater Treatment
Wastewater treatment is carried out in a complex set of steps, in which the wastewater is treated by means of complex biological, physical, and chemical processes. Today, plants are often operated in a conservative and inefficient risk-averse mode, without the ability to quantify the risk or truly minimize the costs. An innovative Operational control applying descriptive (historical data analysis for a simulation model design and plant state estimation); predictive (wastewater process behavior modeled by a transition probability matrix), and prescriptive analytics (Markov Decision Process) was developed. The system was deployed in Lleida (Spain). Use of the system resulted in a dramatic 13.5 percent general reduction in the plant’s electricity consumption; a 14 percent reduction in the amount of chemicals needed to remove phosphorus from the water; and a 17 percent reduction in sludge production.
- IBM: MPA Safer System
With the expected increase in vessel traffic and port capacity, the Singapore Maritime and Port Authority (MPA) has been working to ensure that the future Port of Singapore is safe, secure, efficient and sustainable. Project SAFER, “Sense-making Analytics For maritime Event Response”, is an important component in this effort. A collaboration between MPA and IBM Research, Project SAFER aims to design and develop new analytics capabilities for dramatically increasing the efficiency of maritime operations. The system uses novel cognitive-based analytics leveraging machine learning and entity resolution to provide full situational awareness capability, accurate prediction and intelligence for improving maritime decision-making. Using the SAFER machine-learning-based analytics and vessel prediction models, abnormal and suspicious behaviour is instantly discovered. Based on the extent to which the observed activity of individual or multiple interacting vessels deviates from the modelled behaviour, the event is instantly geo-localized, and sent in the form of an alert. MPA can thus address infringements across all 1000 vessels in real-time SAFER system’s automated movement detection leads to a significant accuracy improvement of 34%. Vessel movement information is needed not only for ensuring safety and security but for many other functions including billing: the accuracy improvement achieved by the SAFER system thus has direct implications on revenue and reducing disputes.
- Macys: A Model Driven Approach to Store Selling Space Optimization
Store Locations sales performance by merchandise business was until now being compared against benchmarks formulated by averages of similar scale. A new approach has been developed integrating Exploratory Analytics (Co-clustering) and Prescriptive Analytics (Non‐Linear Spline Regression Optimization Model and Seasonal (Random Walk) Autoregressive Integrated Moving Average (SARIMA) Model). We have developed a new workflow to integrate all three models in recommending optimal store layouts and merchandize mix for new store locations and major remodels of existing ones. In this three-tiered process, the analyst first identifies the statistical cluster membership of the under analysis location and formulates a plan based on that benchmark. Then he/she invokes the optimization model that provides the space adjustment recommendations that maximize its sales potential based on existing cross‐sectional data (for remodel stores). In the final step, the forecasting model is used to validate whether the recommendations made based on cross‐sectional (historical) data hold true in the time‐frame where these changes (projected store opening or remodel completion) are expected to take place.
- Northwestern University: SAFE (Situational Awareness for Events): A Data Visualization System
Marathons and other endurance events are growing in popularity, and thus require significant resources to ensure safety and success. Event management tools have not grown to meet this need. A team of Northwestern University faculty and students and staff members of the Bank of America Chicago Marathon has developed a data visualization system that incorporates critical data into a user-friendly dashboard to provide a centralized source of information at mass gathering events. This system uses descriptive, predictive and prescriptive analytics to help race organizers and relevant stakeholders effectively manage and oversee all participants, monitor the dynamic location of race participants, and manage health and safety resources throughout the event should any emergency issues arise. Our system is the first comprehensive dashboard for endurance event management. The system provides a dynamic representation of the flow of people and resources. The system integrates real-time dynamic data from tracking devices and predictive algorithms developed by the research team, and presents the information on a summary visual device, both as a large screen in an incident command facility for group monitoring and a desktop/mobile version for individual monitoring. The system has become an integral component in the management of the Bank of America Chicago Marathon and Shamrock Shuffle 8K and the Chevron Houston Marathon and Aramco Half Marathon.
- Schneider: Chassis Leasing and Selection Policy for Port Operations
Port cargo drayage operations manage the movement of shipping containers that arrive and depart on ocean-going container vessels and are transported over the road to and from inland trans-loading facilities. While containers are on land they are placed on wheeled chassis until they return to the port facility. A significant operational challenge is the acquisition and management of these chassis. While many port drayage operators simply lease chassis on a per day basis as demand warrants, Schneider National has determined that an analytics-driven policy that combines long term leasing with daily rental leads to significant cost savings while improving both service and reliability. We present and implement a solution methodology that addresses the two decision problems that arise with this dual sourcing approach: 1) the optimal fleet size for leased chassis and 2) a real-time decision policy for selecting between rental and leased chassis as containers are received. As we demonstrate our solution represents an integrated approach that combines the three general areas of analytics methodology and incorporates a particularly novel interplay of optimization, simulation, and predictive modeling. We conclude with an analysis of the financial benefit that has been achieved and a discussion of the applicability of our methodology to other problem settings.
INFORMS O.R. & Analytics Student Team Competition
Finalist Competition – Conference attendees invited!
The finalists will present on Monday, April 16 from 9:10am-12:45pm in the Kent room. The winners will be announced on Tuesday, April 17 during the networking lunch.
Eight student teams from eight countries across the world will compete as finalists in the 2018 INFORMS OR & Analytics Student Team Competition. This annual INFORMS prize, now in its second year, recognizes outstanding solutions to real-world problems developed by undergraduate and master’s student teams. First Prize is $7,500.
9:10am – Hankuk University of Foreign Studies. Republic of Korea
9:35am – Queen’s University, Canada
10:00am- University of Belgrade, Serbia
10:30am – University of Bern, Switzerland
10:55am – The Hebrew University of Jerusalem, Israel
11:30am – Universidad de los Andes, Colombia
12:00pm – University of North Carolina at Chapel Hill, US
12:25pm – Özyeğin University, Turkey
A panel of 18 academic and industry experts judged written submissions to select the eight finalists; the eight teams will then be judged on their oral presentations and written entries on Monday. Winners will be announced at the conference luncheon on Tuesday.
The competition problem was provided by the Principal, a leading financial investment management company. Principal was looking for insights and innovative ideas on developing a global equity optimization framework to improve risk-adjusted returns. As Title Sponsor, Principal generously supported the competition with funding as well as providing the problem, company data and access to the Principal analytics team.
Additional competition funding comes from Syngenta, Presenting and Founding Sponsor; Syngenta was Title Sponsor for the competition’s 2017 inaugural year. Eleven technology companies offered complimentary access to their software and are named as Software Sponsors: AMPL, FICO, GAMS, GUROBI Optimization, IBM, LINDO Systems, MathWorks, Palisade, Simio, SIMUL8, Tableau.
To find out more: http://connect.informs.org/oratc/home
Syngenta Crop Challenge in Analytics
Today, the agriculture industry works to optimize the amount of food we gain from plants by breeding plants with the strongest, highest-yielding genetics. Scientists at R&D organizations like Syngenta create stronger plants by breeding and then selecting the best offspring over time to provide to farmers. Data-driven strategies can help our industry breed better seeds, faster. Developing models that identify robust patterns in our experimental data may help scientists more accurately choose seeds that increase the productivity of the crops we plant – and help address the growing global food demand.
How can we use data to address the growing global food demand?
The finalists will present on Monday, April 16. The winner will be announced on Tuesday, April 17 during the networking lunch.
Congratulations to the 2018 Finalists!
- “A simple way to predict crop yields, using Multiple Factor Analysis, Random Forests and spatio-temporal weather monthly forecast.” Jacques Ehret and Patrick Vetter Supper & Supper GmbH, Berlin, Germany.
- “Bridging concepts from Bayesian theory, Artificial Intelligence and Genetics: A novel Bayesian Network methodology for predictions and decision-making. ” Jhonathan Pedroso Rigal dos Santos, Sao Paulo, Brazil.
- “Genotype – Environment Interaction (G by E) Analysis Using Deep Neural Networks Approach. Saeed Khaki, Hans Mueller, Lizhi Wang, Iowa State University, Ames, US.
- “Speeding up maize hybrids breeding schemes using machine learning” Andres Aguilar, Sylvain Delerce, Michael Caraccio Lausanne, Juan Camilo Rivera, Maria Camila Gomez Steven Humberto Sotelo, and Anestis Gkanogiannis CIAT, Palmira, Colombia.
- “Using Deep Learning to predict Maize performance” Rodrigo Gonçalves Trevisan, Jackeline Pedriana Borba, and Júlia Silva Morosini. Piracicaba, Brazil.