{"id":205,"date":"2024-07-14T19:51:00","date_gmt":"2024-07-14T19:51:00","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/?page_id=205"},"modified":"2024-09-23T13:43:56","modified_gmt":"2024-09-23T13:43:56","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/","title":{"rendered":"TutORials"},"content":{"rendered":"<!--themify_builder_content-->\n<div id=\"themify_builder_content-205\" data-postid=\"205\" class=\"themify_builder_content themify_builder_content-205 themify_builder tf_clear\">\n                    <div  data-lazy=\"1\" class=\"module_row themify_builder_row tb_y4yf801 tb_first tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_1 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col-full tb_61lm802 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_bgiy806   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <p>The <em>TutORials in Operations Research<\/em> series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meeting.<\/p>    <\/div>\n<\/div>\n<!-- \/module text -->        <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"simulation-optimization\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-simulation-optimization tb_wfca364 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_sxa3365 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_wpik742   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><span style=\"color: #720e20;\"><strong>Nanoretail Operations in Developing Markets<\/strong><\/span><\/h3>\n<p>Across much of the developing world, family-operated nanostores provide daily grocery needs to billions of poorly paid consumers. This highly fragmented retail channel is of critical importance to consumer brands as in many markets this is the largest retail channel. We characterize the empirical context in which these stores operate, as well as the intricate operations that manufacturers and distributors put in place to supply them with their goods. We then elaborate on modeling operations execution and operations strategies to expose critical tradeoffs that are distinct from those in organized retail in developed markets. We discuss research results that have demonstrated why many manufacturers choose to serve this market directly and at high frequency, why manufacturers invest considerable effort in deploying sales agents networks, how this channel manages to remain competitive with modern organized retail such as convenience store chains, and how digitization and novel financing solutions provide a further competitive advantage to the nanoretail channel. Finally, we discuss how to conduct research in this retail segment and provide examples of novel contexts and business models that may open up new areas of nanoretail research.<\/p>\n<p><strong>Speakers: Jan C. Fransoo, Rafael Escamilla, and Jiwen Ge<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_8cro54 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-8cro54-0\" class=\"tb_title_accordion\" aria-controls=\"acc-8cro54-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bios<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-8cro54-0-content\" data-id=\"acc-8cro54-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong>Jan C. Fransoo<\/strong> is Professor of Operations and Logistics Management in the Department of Information Systems &amp; Operations Management at Tilburg University, and holds courtesy affiliations at Eindhoven University of Technology and Massachusetts Institute of Technology. Fransoo has over 30 years of experience in conducting research in operations and supply chain management, making use of a wide variety of analytical, quantitative, and qualitative methods. His research is widely published in and cited by the top operations management and other high-impact academic journals, and in several of his (edited) books, notably \u201cSustainable Supply Chain Management\u201d, \u201cReaching 50 Million Nanostores\u201d, and \u201cBehavioral Operations in Planning and Scheduling\u201d. His current research interests focus on retail operations in developing markets, and on the role of humans in AI-enhanced decision making. <br>Fransoo holds an MSc in Industrial Engineering and a PhD in Operations Management from Eindhoven University of Technology.<\/p>\n<p><strong><b>Rafael Escamilla\u00a0<\/b><\/strong>holds the position of Assistant Professor in Supply Chain Management at the W.P. Carey School of Business at Arizona State University. He specializes in retail supply chains within emerging markets, employing field experiments and econometric techniques to investigate the factors that impact supply chain decision-making. Rafael\u2019s academic background includes a joint PhD degree from Tilburg University and Kuehne Logistics University, a Graduate Certificate in Logistics and Supply Chain Management from MIT, a Master of Science from the Universit\u00e9 de Technologie de Troyes, and a Bachelor of Engineering from Tecnol\u00f3gico de Monterrey. Rafael has taught courses on topics such as Logistics, Causal Inference, Supply Chain Strategy, Data Science, Research Methods, and Project Management. His work has received support from industry collaborations, and he has conducted research as a visiting scholar at the Wharton School, University of Pennsylvania.<\/p>\n<p><strong><b>Jiwen Ge<\/b><\/strong> is an associate professor at the Institute of Supply Chain Analytics, Dongbei University of Finance and Economics. Previously, he was a post-doctoral research fellow at the Tuck School of Business, Dartmouth College, and completed his PhD at Eindhoven University of Technology in the Netherlands. His research focuses on emerging-market retail, examining operations in traditional nanostore channels, modern organized offline channels, and e-commerce. He employs both analytical modeling and empirical methodologies in his work.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_qc1a221 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"mip-approaches-submodularity\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-mip-approaches-submodularity tb_eqvi432 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_yobf432 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_3mkg43   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Humanitarian Operations and Earmarked Funding<\/h3>\n<p>Earmarked funding, also referred to as restricted funding, is one of the main characteristics of humanitarian operations. Earmarked funding can be defined as the donors\u2019 contributions to humanitarian organizations to be used for a specific purpose. This is in contrast to flexible funding, which can be used for any purpose. This tutorial introduces the trade-off between total donations and operational performance due to humanitarian earmarking. The tutorial explains why allowing donors to earmark their contributions helps organizations increase fundraising effectiveness. It also explains why earmarking hurts organizations\u2019 per-dollar performance. Because humanitarian organizations\u2019 utility increases in total donations and per-dollar (or any other currency) performance, the best fundraising strategy for the organizations, collecting earmarked or flexible funds, is not apparent. Moreover, this tutorial argues that earmarked funding is here to stay and discusses models that reduce the negative effect of earmarked funding. Then, it proposes that a thoughtful mix of earmarked and flexible donations may be the best way for humanitarian organizations to fund their operations. The balance between earmarked and flexible funds depends on reducing the negative operational consequences of earmarked funds. The tutorial concludes by identifying areas for future research on humanitarian operations and earmarked funding.<\/p>\n<p><strong>Speaker: Alfonso J. Pedraza-Martinez<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_plo636 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-plo636-0\" class=\"tb_title_accordion\" aria-controls=\"acc-plo636-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-plo636-0-content\" data-id=\"acc-plo636-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Alfonso Pedraza-Martinez, PhD<\/b><\/strong> (INSEAD, 2011), directs the HOPE &#8211; Humanitarian Operations Lab at the University of Notre Dame. His research has informed international humanitarian organizations and faith-based relief organizations. He teaches humanitarian operations analytics at the MBA and PhD levels and enjoys directing undergraduate research projects. Alfonso received the 2022 Luk Van Wassenhove Career Award from the European Working Group on Humanitarian Operations. He has edited special issues in Production and Operations Management, the Journal of Operations Management, and the European Journal of Operational Research. Professor Pedraza-Martinez is a former president of the College of Humanitarian Operations and Crisis Management at the Production and Operations Management Society.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_pcdv207 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"pharma-supply-chains\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-pharma-supply-chains tb_yx9n893 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_4xs0894 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_fxml629   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Nonconvex, Nonsmooth, and Nonregular Optimization: A Computational Framework 9299<\/h3>\n<p>Algebraic modeling languages presently lack the ability to effectively support the<br>formulation and solution of nonconvex and nonsmooth optimization problems. Since<br>an arbitrary problem of this kind is intractable, any hope to achieve practically useful<br>solutions would rely on means to convey specific problem structure to an algorithm.<br>In this tutorial, we present a framework for specifying nonconvex, nonsmooth, and<br>nonregular problems within an algebraic modeling language that makes available the<br>key structural properties to an algorithm. It also facilitates experimentation with<br>different model formulations and algorithmic approaches. The framework entails a<br>change of mindset away from the traditional formulation of objective and constraint<br>functions, and instead asks the analyst to specify a basic feasible set, a basic objective<br>function, one or more monitoring functions, and several performance functions. Eleven<br>examples ranging from goal programming to variational inequalities and engineering<br>risk analysis illustrate the practical implications of the framework. <\/p>\n<p><strong>Speaker:\u00a0<\/strong><strong>Michael C. Ferris, Olivier Huber, and Johannes O. Royset<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_9mi7342 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-9mi7342-0\" class=\"tb_title_accordion\" aria-controls=\"acc-9mi7342-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-9mi7342-0-content\" data-id=\"acc-9mi7342-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Michael C. Ferris<\/b><\/strong> holds the John P. Morgridge Chair in Computer Sciences, and is the Jacques-Louis Lions Professor of Computer Sciences at the University of Wisconsin, Madison, USA. He is the Director of the Data Sciences Hub within the Wisconsin Institutes for Discovery.\u00a0 He received his PhD from the University of Cambridge, England in 1989.Dr. Ferris&#8217; research is concerned with algorithmic and interface development for large scale problems in mathematical programming, including links to the GAMS and AMPL modeling languages, and general purpose software such as PATH, NLPEC and EMP.\u00a0 He has worked on many applications of both optimization and complementarity, including covid vaccine delivery, cancer treatment planning, energy modeling, economic policy, traffic and environmental engineering, video-on-demand data delivery, structural and mechanical engineering. Ferris is a SIAM fellow, an INFORMS fellow,\u00a0received the Beale-Orchard-Hays prize from the Mathematical Programming Society and is a past recipient of a NSF Presidential Young Investigator Award, and a Guggenheim Fellowship.\u00a0 He serves\u00a0on the editorial boards of Informs Journal on Computing\u00a0 and Optimization Methods and Software.<\/p>\n<p><strong>Dr. Olivier Huber<\/strong> is a staff scientist at the University of Wisconsin, Madison, USA. <br>He completed his PhD at INRIA Grenoble, France in 2015. <br>His research interests are in the development of modeling and algorithmic frameworks for complex optimization problems.<\/p>\n<p><strong><b>Dr. Johannes O. Royset<\/b><\/strong> is a professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at University of Southern California. He was awarded a Young Investigator Award from the Air Force Office of Scientific Research in 2007, and the Barchi Prize as well as the MOR Journal Award from the Military Operations Research Society in 2009. He received the Carl E. and Jessie W. Menneken Faculty Award for Excellence in Scientific Research in 2010 and the Goodeve Medal from the Operational Research Society in 2019. Professor Royset was a plenary speaker at the International Conference on Stochastic Programming in 2016, the SIAM Conference on Uncertainty Quantification in 2018, and the INFORMS Security Conference in 2022. He has a Doctor of Philosophy degree from the University of California at Berkeley (2002). Professor Royset has been an associate or guest editor of SIAM Journal on Optimization, Operations Research, Mathematical Programming, Journal of Optimization Theory and Applications, Naval Research Logistics, Journal of Convex Analysis, Set-Valued and Variational Analysis, and Computational Optimization and Applications. He has published two books and more than 100 articles.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_ls53654 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"sensitivity-analysis\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-sensitivity-analysis tb_0m7t570 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_l7b0571 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_k72z718   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><span style=\"color: #720e20;\"><strong>Experimental Design for causal Inference Through an Optimization Lens<\/strong><\/span><\/h3>\n<p>AThe study of experimental design offers tremendous benefits for answering causal ques tions across a wide range of applications, including agricultural experiments, clinical trials, industrial experiments, social experiments, and digital experiments. While valu able in such applications, the costs of experiments often drive experimenters to seek more efficient designs. Recently, experimenters have started to examine such efficiency questions from an optimization perspective, as experimental design problems are fun damentally decision-making problems. This perspective offers a lot of flexibility in leveraging various existing optimization tools to study experimental design problems. thus aims to examine the foundations of experimental design problems in the context of causal inference as viewed through an optimization lens.<\/p>\n<p><strong>Speaker: Jinglong Zhao<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_fzq3775 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-fzq3775-0\" class=\"tb_title_accordion\" aria-controls=\"acc-fzq3775-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-fzq3775-0-content\" data-id=\"acc-fzq3775-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong>Jinglong Zhao<\/strong> is an Assistant Professor of Operations and Technology Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_6f8i483 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"stockpyl\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-stockpyl tb_f7j6514 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_4pgr514 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_mgd6654   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Machine Learning Methods for Large Population Games<\/h3>\n<p>In this tutorial, we provide an introduction to machine learning methods for finding Nash equilibria in games with large number of agents. These types of problems are important for the operations research community because of their applicability to real life situations such as control of epidemics, optimal decisions in financial markets, electricity grid management, or traffic control for self-driving cars. We start the tutorial by introducing stochastic optimal control problems for a single agent, in discrete time and in continuous time. Then, we present the framework of dynamic games with finite number of agents. To tackle games with a very large number of agents, we discuss the paradigm of mean field games, which provides an efficient way to compute approximate Nash equilibria. Based on this approach, we discuss machine learning algorithms for such problems. First in the context of discrete time games, we introduce fixed point based methods and related methods based on reinforcement learning. Second, we discuss machine learning methods that are specific to continuous time problems, by building on optimality conditions phrased in terms of stochastic or partial differential equations. Several examples and numerical illustrations are provided along the way.<\/p>\n<p><strong>Speakers: G\u00f6k\u00e7e Dayanikli and Mathieu Lauri\u00e8re<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_qotz458 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-qotz458-0\" class=\"tb_title_accordion\" aria-controls=\"acc-qotz458-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-qotz458-0-content\" data-id=\"acc-qotz458-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>G\u00f6k\u00e7e Dayan\u0131kl\u0131<\/b><\/strong> is an Assistant Professor of Statistics at the University of Illinois Urbana-Champaign where she is also an Affiliate Faculty in the Department of Industrial &amp; Enterprise Systems Engineering. Before joining UIUC, she worked as a Term Assistant Professor at Columbia University, Department of Statistics. She completed her Ph.D. in Operations Research &amp; Financial Engineering at Princeton University where she was the recipient of School of Engineering and Applied Science Award for Excellence. During Fall 2021, she was a visiting graduate researcher at the Institute for Mathematical and Statistical Innovation (IMSI) to participate in the Distributed Solutions to Complex Societal Problems program.<\/p>\n<p>\u00a0<strong><b>Mathieu Lauri\u00e8re<\/b><\/strong> is an Assistant Professor of Mathematics and Data Science at NYU Shanghai. Prior to joining NYU Shanghai, he was a Postdoctoral Research Associate at Princeton University in the Operations Research and Financial Engineering (ORFE) department. He obtained his MS from Sorbonne University and ENS Paris-Saclay and his PhD from the University of Paris. Before joining Princeton University, he was a Postdoctoral Fellow at the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai. Most recently, Mathieu was a Visiting Faculty Researcher at Google Brain, for the Brain Team (Paris).<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_l6tv420 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"supply-chain-centric-view\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-supply-chain-centric-view tb_fu02399 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_5p7p399 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_ds215   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><span data-sheets-formula-bar-text-style=\"font-size:13px;color:#000000;font-weight:bold;text-decoration:none;font-family:''Arial'';font-style:normal;text-decoration-skip-ink:none;\">Food Bank Operations: A U.S. Perspective on Humanitarian Food Assistance<\/span><\/h3>\n<p>Food banks are non-profit organizations with the primary mission of providing food assistance to the communities they serve. This assistance occurs through the collection, storage, and distribution of food to people in need through a complex supply chain network of donors and charitable agencies. Distribution of food is challenging\u00a0in this environment, given the many resource constraints experienced at the financial, human, and material levels. Furthermore, the COVID-19 pandemic highlighted\u00a0the important role these organizations play as demand for food assistance surged,\u00a0while sources of supply were constrained. This tutorial provides an overview of food\u00a0bank operations from a supply chain perspective. We specifically characterize the key\u00a0stakeholders, product, and information flows within the food bank supply chain and\u00a0draw from our prior experience with several U.S. food banks to delineate structural\u00a0differences that exist among these supply chain networks. We further elucidate the\u00a0influence of the supply chain network design on the organizations\u2019 operational decisions and strategic direction with respect to equity, efficiency, effectiveness, diversity\u00a0and inclusion. We present several studies that illustrate the role of descriptive, predictive, and prescriptive analytics in improving the distribution of food and reducing\u00a0food waste, as well as provide insights for future research in this area.<\/p>\n<p><strong>Speakers:\u00a0Lauren Davis, Irem Sengul Orgut, Steven Jiang, Eric Aft, Charlie Hale, Larry Morris, Jean Rykaczewski<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_1qsg899 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-1qsg899-0\" class=\"tb_title_accordion\" aria-controls=\"acc-1qsg899-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-1qsg899-0-content\" data-id=\"acc-1qsg899-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Lauren Davis<\/b><\/strong> is a professor in the Department of Industrial and Systems Engineering at North Carolina A&amp;T (NCAT). She received her Ph.D. in Industrial Engineering from North Carolina State University.\u00a0 Prior to joining NCA&amp;T she spent 12 years as a senior software engineer at IBM\u00a0 supporting SAP implementations in Research Triangle Park (NC), Guadalajara (Mexico) and Shenzen (China). Her research focuses on stochastic modeling of supply chain systems in for-profit and non-profit settings, with application areas in hunger relief, humanitarian logistics, and homeland security. Her work has appeared several peer reviewed journal publications including European Journal of Operational Research, International Journal of Production Economics and the Journal of Humanitarian Logistics and Supply Chain Management. Her work has been supported by the National Science Foundation, Department of Homeland Security, and US Department of Agriculture.\u00a0 Dr. Davis is an active member of INFORMS and has served in various roles including the President of the Minority Issues Forum, chair of volunteer service award committee, and chair of the AMAZON SCOT Scholarship committee. She currently serves as an associate editor for Transportation Science and serves s as a board member of the Second Harvest Food Bank of Northwest North Carolina.<\/p>\n<p><strong><b>Dr. Irem Sengul Orgut<\/b><\/strong> is an Assistant Professor of Operations Management and an Assistant Director at the Institute of Data and Analytics (IDA) at Culverhouse College of Business at the University of Alabama. She received her Ph.D. in Industrial Engineering with a minor in Statistics at North Carolina State University, Raleigh, NC in 2015. Prior to joining UA, she worked as Analytics Project Manager at Lenovo, Morrisville, NC. Her research focuses on using analytics and operations research methods to address problems involving multiple decision-makers with conflicting objectives and various sources of uncertainty in humanitarian operations management with an emphasis on hunger relief operations. In her role at IDA, she has founded and directs the UPWARD (Underserved Populations Workgroup for Analytics Research and Development) Initiative that uses data and analytics methods to solve unique challenges humanitarian and nonprofit organizations face and alleviate the well-being of underserved populations. Dr. Sengul Orgut\u2019s research has appeared in journals including the <em><i>European Journal of Operational Research<\/i><\/em> and <em><i>Production and Operations Management<\/i><\/em>, and it has been supported by the National Science Foundation. Dr. Sengul Orgut received the University of Alabama&#8217;s Excellence in Community-Engaged Scholarship Award for her work in the UPWARD initiative. She serves as the secretary of the Women in OR\/MS (WORMS) forum at INFORMS and is on the editorial review board of the <em><i>Decision Sciences Journal<\/i><\/em>.<\/p>\n<p><strong><b>Steven Jiang<\/b><\/strong> is an Associate Professor in the Department of Industrial and Systems Engineering at North Carolina Agricultural and Technical State University. He received his PhD in Industrial Engineering from Clemson University in 2001. His research interests include human systems integration, supply chain, visual analytics, and cognitive engineering.<\/p>\n<p>He has authored or coauthored more than 150 technical publications in refereed journals and refereed conference proceedings. Dr. Jiang has received research funding from the National Science Foundation, US Army research laboratory, US Department of Transportation, US Airforce Laboratory, North Carolina Department of Transportation, and US Department of Homeland Security. Dr. Jiang is on the Editorial Board of the International Journal of Industrial Ergonomics.<\/p>\n<p><strong><b>Eric A. Aft <\/b><\/strong>has over 34 years in nonprofit leadership roles, Eric feels privileged to lead Second Harvest Food Bank of Northwest North Carolina as its third CEO.\u00a0 He has been in this role since 2018.\u00a0During Eric\u2019s six-year tenure at SHFBNWNC, he has focused the organization on integrating immediate response activities with root-cause initiatives, including enhanced partnerships with health systems, colleges, school systems, expanded workforce training, and heightened the focus on nutrition education and outreach along with creative food distribution strategies. In addition, he has overseen the completion of a $25 million new headquarters building with no debt and the organization\u2019s first-ever satellite location. Eric also established a more equitable wage structure within the organization and enhanced growth opportunities for team members.\u00a0Recent organizational and personal recognitions include:<\/p>\n<ul>\n<li>Winner of Amazon Web Services IMAGINE Grant Go Further, Faster Award (2023\/2024)<\/li>\n<li>Green Business of the Year by the Piedmont Environmental Alliance (2023)<\/li>\n<li>Triad Business Journal\u2019s Leaders in Diversity Award (2023)<\/li>\n<li>Runner-up for Best Commercial Real Estate (Industrial) Projects (2022)<\/li>\n<li>Triad Business Journal\u2019s C-Suite Award Winner (2020)\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/li>\n<\/ul>\n<p>Prior to joining Second Harvest, Eric worked in the United Way system for 26 years, including 11 year as chief executive officer for two United Way organizations \u2013 one in Virginia and one in Ohio. He also served briefly as a member of the Wake Forest University Advancement team.\u00a0 Eric also appreciates the opportunity to serve for the past seven years as an adjunct faculty member at Salem College.\u00a0He is a graduate of Rhodes College in Memphis and holds a Master\u2019s degree from the University of South Carolina in Health Administration.\u00a0<\/p>\n<p><strong><b>Charlie Hale<\/b><\/strong> joined the Food Bank of Central &amp; Eastern North Carolina in March 2005 as the IT Director and became Vice President of IT &amp; Operations in 2007.\u00a0 Charlie graduated from NC State University in 1989 with a Bachelor of Science degree in Meteorology.\u00a0 Prior to joining the Food Bank Charlie as the IT Director at Smithfield Premium Genetics. Since joining the Food Bank, Charlie has worked on many projects to enhance the organization including infrastructure expansion, growth in equitable food distribution, raising the bar on food safety, and disaster relief operations.\u00a0 Charlie\u2019s current role with the Food Bank is Chief Operating Officer which includes the areas of facilities maintenance, transportation, food safety &amp; warehouse compliance, distribution &amp; inventory control, child and senior nutrition programs, and food sourcing &amp; network services.<\/p>\n<p><strong><b>Larry Morris<\/b><\/strong> has served as the Director of Network Engagement at the Food Bank of Central &amp; Eastern North Carolina. He has worked with the Food Bank since 1993.\u00a0He is the Food Bank\u2019s technical expert for agency compliance issues related to Food Bank and Feeding America policies. He leads a team that is responsible for ensuring agency engagement and compliance for nearly 650 partner agencies in a 34-county service area. He also established the Food Bank\u2019s Partner Agency Advisory Council in 2017.\u00a0Larry has extensive experience in Food Bank disaster relief efforts in response to hurricanes, ice storms, tornadoes, and the COVID-19 pandemic.\u00a0Larry also has served on Feeding America\u2019s Agency Relations Council and serves on the local Emergency Food &amp; Shelter Board for Wake County. He helped to coordinate the Food Bank\u2019s participation in five national hunger research studies from 1993 through 2013. He has presented numerous times at Feeding America conferences; sharing information and experiences from the Food Bank of Central &amp; Eastern North Carolina with other agency relations professionals from food banks throughout the U.S.\u00a0In his spare time, Larry enjoys spending quality time with his family, working out, gardening and reading anything related to astronomy.<\/p>\n<p><strong>Jean Rykaczewski <\/strong>joined the West Alabama food Bank in 2016 as Executive Director and becoming CEO in 2021. Jean graduated Indiana University of PA and UA. Prior to joining the Food Bank Jean has taken a failing Food Bank, to a successful Food Bank that was debt free. In 2023 moved the Food Bank to a new location doubling the square footage, and building a 540 pallet freezer and cooler. Before and including the move Jean restructured systems and operations, expanded the growth in equitable food distribution especially throughout the Black Belt where poverty rates are 37-40% , and improved disaster relief operations. Jean will be introducing a teaching kitchen and Food Pharmacy opening this fall.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion --><!-- module text -->\n<div  class=\"module module-text tb_srrm53   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Combining Large Language Models and OR\/MS to Make Smarter Decisions<\/h3>\n<p>Operations Research\/Management Science (OR\/MS) capabilities can provide tremendous value in helping enterprises and individuals make smarter decisions. However, the creation and deployment of OR\/MS-based decision-making solutions require significant time and expertise, making their widespread application challenging. Large language models (LLMs), exemplified by models such as ChatGPT, Gemini, and Claude, are deep neural network models encompassing billions to trillions of parameters. These models are pre-trained with a vast scope of general knowledge and are quickly adaptable to many downstream tasks. Beyond core capabilities like document summarization and code generation, LLMs exhibit emerging capabilities, such as learning new tasks from a few natural language examples. Generative AI technologies like LLMs, therefore, have transformative potential for many fields and professions. In this work, we explore the potential of LLMs and their capabilities to significantly improve the creation of OR\/MS based decision-making solutions. After providing relevant technical background on LLMs, we show this potential through three concrete and detailed examples: using LLMs to significantly reduce the time required to create decision-making applications while improving their quality; using LLMs to extract structured information from unstructured text without the need to create new natural language processing models, a capability important, for example, for demand forecasting; and using LLMs to drive natural language-based interfaces enabling a business user to easily and flexibly interact with analytical models used for decision making. As LLMs are a new technology that introduces new risks, this work also provides guidelines for their productive, ethical and responsible use and describes ongoing developments relevant to the OR\/MS profession. We hope this work will serve as a starting point both for the application of LLMs to the OR\/MS use cases we described and as the starting point for exploring new and exciting integrations between LLMs and OR\/MS, ultimately enabling the use of OR\/MS to make smarter decisions in a much more widespread manner.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<p><strong>Segev Wasserkrug, Leonard David Jean Boussioux, and Wei Sun<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_b0lh514 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-b0lh514-0\" class=\"tb_title_accordion\" aria-controls=\"acc-b0lh514-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-b0lh514-0-content\" data-id=\"acc-b0lh514-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Segev Wasserkrug<\/b><\/strong> is a Senior Technical Staff Member and technical leader at IBM Research and holds a Research Fellow position at the Technion&#8217;s Faculty of Data and Decision Sciences. He is an expert in optimization, game theory, and machine learning, with a current focus on enhancing decision-making by integrating Foundation Models and Large Language Models with Operations Research and Management Science.Segev has over two decades of experience in utilizing advanced analytics, AI, and optimization across various domains to help IBM and its clients make better decisions. Segev holds a strong academic record in operations research, computer science and AI, with over 50 academic publications and 45 patent submissions. He is the winner of the DEBS 2018 test of time award, a three-time INFORMS Wagner Prize finalist and was a finalist for the 2018 INFORMS Innovative Application in Analytics Award. Segev received his Ph.D. in Information Systems Engineering, and also holds M.Sc. and B.A. degrees in Computer Science from the Technion.<\/p>\n<p><strong><b>L\u00e9onard Boussioux<\/b><\/strong> is an assistant professor in Information Systems and Operations Management at the University of Washington&#8217;s Foster School of Business, with an adjunct position at the Allen School of Computer Science &amp; Engineering. He holds a PhD in Operations Research from MIT and focuses his research on applying analytics and artificial intelligence (AI) to healthcare, climate, sustainability, and societal improvement. Additionally, L\u00e9onard is an affiliated faculty at the Laboratory for Innovation Science at Harvard, where he explores creative problem-solving and AI&#8217;s impact on the future of work and innovation.\u00a0His research and entrepreneurial work have received wide recognition, including giving two TEDx talks, winning the MIT 3-minute thesis competition, and awards from INFORMS, MIT, IEEE, UNESCO, and Google. He previously worked at Google X, Mila, UC Berkeley, and the French National Centre for Scientific Research. L\u00e9onard is also a passionate teacher and has received five teaching awards at the University of Washington and MIT, including the Goodwin Medal.<\/p>\n<p><strong><b>\u00a0<\/b><\/strong><strong><b>Wei Sun<\/b><\/strong> is a senior research scientist at IBM T. J. Watson Research Center in NY. Her research focuses on the intersection of machine learning and optimization, encompassing causal decision-making, constrained predictions, and game theory. Her work has been instrumental in addressing real-world challenges for companies in digital marketing, travel\/transportation, and financial services.\u00a0Wei holds a Ph.D. in operations research and an SM in computational design and optimization from MIT. Additionally, she has an SM in computational engineering and a B.Eng. in electrical and computer engineering with First-Class Honors from the National University of Singapore.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_tt6d47 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"capturing-emerging-targets\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-capturing-emerging-targets tb_14ba518 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_a7b1518 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_a5vr363   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Interventions for Patients with Complex Medical and Social Needs<\/h3>\n<p>Patients with multiple chronic conditions and social needs represent a small percentage of the population but have a disproportionate impact on healthcare costs and utilization. Organizations around the United States have created programs \u2013 often referred to as complex care interventions \u2013 to improve the health and well-being of such patients and reduce avoidable hospital and emergency department use. In this tutorial, we focus on two emerging themes in the field: (1) identifying clinically meaningful subgroups in complex care populations through unsupervised learning methods; and (2) describing the key operational features of interventions with an emphasis on staffing needs and the impact on patient outcomes. The material presented in this tutorial draws on the research of the Healthcare Operations Research Lab at the University of Massachusetts, Amherst, and its collaborating partners. To illustrate these themes and contextualize the details of complex care delivery, we use a range of patient-level examples, visualizations, descriptive summaries, case studies, and results from the clinical literature.<\/p>\n<p><strong>Speakers: Hari Balasubramanian, Sindhoora Prakash, Ali Jafari, Arjun Mohan, and Chaitra Gopalappa<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_ix7l591 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-ix7l591-0\" class=\"tb_title_accordion\" aria-controls=\"acc-ix7l591-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-ix7l591-0-content\" data-id=\"acc-ix7l591-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Dr. Hari Balasubramanian<\/b><\/strong> is Professor of Industrial Engineering at the University of Massachusetts, Amherst. His research utilizes methods in optimization, simulation, stochastic modeling, and statistical learning to improve healthcare delivery. Past projects have included capacity planning and scheduling in outpatient, inpatient, and surgical care. His recent work is on detecting population level patterns in the prevalence of multiple chronic conditions and evaluating interventions. Dr. Balasubramanian&#8217;s research has been funded by multiple grants from the National Science Foundation (NSF), including an NSF CAREER award (2013-2019) focused on improving primary care delivery.<\/p>\n<p><strong><b>Sindhoora Prakash<\/b><\/strong> is a PhD student in the Industrial Engineering and Operations Research program at the University of Massachusetts, Amherst. Her current research, funded by the NSF, focuses on analyzing and optimizing intervention delivery for patients with complex medical and social needs. She holds a Bachelor of Technology in Information Technology from the National Institute of Technology Karnataka, Surathkal. Sindhoora has also worked as a Data Scientist at Tesco, where she led operations research initiatives to solve problems in the retail domain, such as markdown pricing, space optimization, and floor planning.<\/p>\n<p><strong><b>Ali Jafari<\/b><\/strong> is a PhD student in the Industrial Engineering and Operations Research program at the University of Massachusetts, Amherst. His current research focuses on healthcare, specifically analyzing the connections between various diseases and disease groups to identify previously unknown relationships. Ali holds a Bachelor&#8217;s degree in Electrical Engineering from K.N. Toosi University of Technology in Tehran, Iran. His growing interest in operations research led him to complete a Master&#8217;s degree in Industrial Engineering from the University of Tehran, Iran, and pursue doctoral studies in the field.<\/p>\n<p><strong><b>Arjun Mohan<\/b><\/strong> currently works as a Supply Chain Analyst at AFI Furnishings. He holds a Master\u2019s in Industrial Engineering and Operations Research from the University of Massachusetts, Amherst. He also has a Bachelor&#8217;s in Chemical Engineering from Mumbai University, India. His research focuses on multimorbidity, specifically the identification of complex combinations of conditions and patterns in comorbidities. He has previously worked as a Data Scientist at CRISIL, Mumbai, and EXL Services Inc., Jersey City. His work has primarily involved the redesign and optimization of operational procedures, and the design of business processes using predictive modeling and decision analytics.<\/p>\n<p><strong><b>Dr. Chaitra Gopalappa<\/b><\/strong> is an associate professor of industrial engineering at the Department of Mechanical and Industrial Engineering and Commonwealth Honors College at the University of Massachusetts, Amherst. She is also a guest researcher at the U.S. Centers for Disease Control and Prevention. Her research is in areas of data analytics, predictive modeling, and control optimization applied to health and social equity research. Her lab is funded by grants from the National Institutes of Health, the National Science Foundation, the U.S. Centers for Disease Control and Prevention, and the World Health Organization.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_hwky496 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"integer-programming-games\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-integer-programming-games tb_ozk0931 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_hvxb931 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_79nc465   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Randomized Rounding Approaches to Online Allocation, Sequencing, and Matching<\/h3>\n<p>Randomized rounding is a technique that was originally used to approximate hard offline discrete optimization problems from a mathematical programming relaxation. Since then it has also been used to approximately solve sequential stochastic optimization problems, overcoming the curse of dimensionality. To elaborate, one first writes a tractable linear programming relaxation that prescribes probabilities with which actions should be taken. Rounding then designs a (randomized) online policy that approximately preserves all of these probabilities, with the challenge being that the online policy faces hard constraints, whereas the prescribed probabilities only have to satisfy these constraints in expectation. Moreover, unlike classical randomized rounding for offline problems, the online policy\u2019s actions unfold sequentially over time, interspersed by uncontrollable stochastic realizations that affect the feasibility of future actions. This tutorial provides an introduction for using randomized rounding to design online policies,<\/p>\n<p><strong>Speaker: Will Ma<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_cdyb823 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-cdyb823-0\" class=\"tb_title_accordion\" aria-controls=\"acc-cdyb823-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-cdyb823-0-content\" data-id=\"acc-cdyb823-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Will Ma<\/b><\/strong> is the Roderick H. Cushman Associate Professor of Business at the Graduate School of Business, Columbia University. His research centers around online algorithms in e-commerce systems, both for supply-side problems like inventory and fulfillment, and revenue management problems like dynamic assortment optimization. He specializes in designing simple online algorithms with performance guarantees, that can be tuned to historical data. Will also has miscellaneous experience as a professional poker player, video-game startup founder, and karaoke bar pianist.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_tq50442 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"efficacy-amazon-recommendations\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-efficacy-amazon-recommendations tb_t40d478 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_xf5r478 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_owlk866   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3>Measuring the Efficacy of Amazon\u2019s Recommendation Systems<\/h3>\n<p>Amazon\u2019s Fulfillment By Amazon (FBA) program provides assistance to Selling Partners (\u201csellers,\u201d for short) in the form of information sharing, recommendations guiding seller actions (e.g., restock quantity recommendations, excess inventory recommendations), and delegated actions (e.g., automated removals of aged inventory). Amazon\u2019s vision is to help sellers make better decisions and achieve better business outcomes.<br>In this tutorial, we consider the sophisticated optimization models Amazon employs to generate recommendations. For example, if a seller has excess inventory, Amazon recommends actions to increase their sell-through rate, such as creating a sale or Sponsored Product ad. We demonstrate how we measure the efficacy of these recommendation systems on seller-product outcomes (e.g., revenue, units shipped, and customer clicks on product listings, or \u201cglance views\u201d). Measuring such outcomes is a causal inference problem because we only observe each seller-product\u2019s \u201cfactual\u201d and not their \u201ccounterfactual\u201d outcome. We employ causal machine learning methodologies such as double machine learning, causal forest, and doubly-robust forest to separate selection bias from a comparison of \u201ctreatment\u201d and \u201ccontrol\u201d sellers. For example, we find that aligning with the restock and excess inventory recommendations, on average, improves several seller-salient outcomes. We also present methods for measuring heterogeneity in the efficacy of these recommendations across seller and product segments, and estimate personalized benefits for each seller-product. Finally, through A\/B testing, we find that sharing quantified efficacy information with sellers increases their adoption of Amazon recommendations. Sellers are responding to this messaging, and the duty to them is to rigorously identify causal estimates.<\/p>\n<p><strong>Speakers:<\/strong><\/p>\n<p><strong>Ozalp Ozer, Serdar Simsek, Xiaoxi Zhao, Ethan Dee, and Vivian Yu<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_abd1315 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-abd1315-0\" class=\"tb_title_accordion\" aria-controls=\"acc-abd1315-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-abd1315-0-content\" data-id=\"acc-abd1315-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong>\u00d6zalp \u00d6zer<\/strong> is an executive leader at Amazon, where he oversees the development of science to optimize Amazon&#8217;s core supply chain. Leading a team of product managers and scientists\u2014including operations researchers, economists, data scientists, and ML experts\u2014he develops optimization algorithms and predictive models that drive the Fulfillment-By-Amazon (FBA) business and also the recently announced Supply Chain by Amazon. His work spans key areas such as seller recommendation systems (e.g., replenishment, advertising, pricing), resource management systems (e.g., inbound and capacity management), and voice of seller systems (e.g., experimentation, sentiment analysis). Dr. \u00d6zer is also a chaired Professor of Management Science, with prior faculty positions at the UTD, MIT, Columbia University, and Stanford University. His expertise includes global value chain management, entrepreneurship, innovation, strategic investment, capacity and inventory planning, market timing, distribution channel management, procurement contract design, and retail and pricing management. He has published extensively in top journals, using tools from Operations Research, Economics, Statistics, Machine Learning, and Management Science. He has received numerous prestigious awards, including the George and Fonsa Brody Professorship, Ashbel Smith Professorship, Wickham Skinner Early-Career Research Accomplishment Award, Hellman Faculty Fellowship, and Terman Faculty Fellowship. Recognized as a Favorite Professor by Poets &amp; Quants, he has earned teaching awards from MIT, Columbia, and Stanford. He is the editor of The Oxford Handbook of Pricing Management and currently serves as an associate editor for <em>Management Science, M&amp;SOM, Operations Research<\/em>, and <em>Production and Operations Management<\/em>. Dr. \u00d6zer\u2019s industry consulting experience includes work with General Motors, Hewlett Packard, Hitachi GST, IBM, Neiman Marcus, and University Hospitals of Cleveland. He holds Ph.D. and M.S. degrees from Columbia University.<\/p>\n<p>\u00a0<strong>Serdar \u015eim\u015fek<\/strong> is an Associate Professor of Operations Management at the Jindal School of Management, University of Texas at Dallas, and an Amazon Visiting Academic. His research focuses on pricing, revenue management, and supply chain management problems, primarily in the retail industry and business-to-business markets. His methodological expertise spans various empirical methods (e.g., causal inference, structural estimation, Bayesian estimation, machine learning), economic theories, and Operations Research tools. He extensively collaborates with companies and leverages their data to develop practical and impactful solutions. His work has been published in leading journals such as <em>Management Science, M&amp;SOM<\/em>, <em>Operations Research, <\/em>and <em>Production and Operations Management<\/em>. At Amazon, Dr. \u015eim\u015fek works within the Fulfillment-By-Amazon (FBA) Science team, collaborating with scientists and product managers from various teams across the company. He has significantly contributed to several projects aimed at measuring and enhancing the efficacy of assistance Amazon provides to third-party sellers. Additionally, he works on developing science-driven products in areas such as capacity planning, procurement, and pricing\/promotion management. He received his B.S. degree from Bilkent University, and Ph.D. and M.Phil degrees from Columbia University&#8217;s Graduate School of Business.<\/p>\n<p>\u00a0<strong>Xiaoxi Zhao<\/strong> is an Economist on the FBA Science team at Amazon. He earned his Ph.D. in Economics in 2017 from the University of Pittsburgh, specializing in applied microeconometrics at the intersection of environmental, urban, and health economics. After graduation, he joined Amazon Web Services (AWS), where he applied advanced econometric techniques to quantify and improve customers&#8217; cloud journeys and experiences. Two years later, he transitioned to his current role on the FBA Science team, where he leads a group of economists. In this position, he focuses on leveraging scalable causal machine learning techniques to generate insights that drive seller performance and business growth.<\/p>\n<p>\u00a0<strong>Ethan Dee<\/strong> is an Economist at Amazon. He holds a Ph.D. in Economics (2023) from the University of Illinois Urbana-Champaign, specializing in applied microeconometrics. His dissertation tackles the natural language processing task of classifying U.S. federal and state legislation into policy areas using a synthesis of supervised and unsupervised machine learning models, with an emphasis on model explainability and scalability. His dual expertise in causal inference and machine learning motivate his focus on causal machine learning methodologies. At Amazon, he applies large, high-dimensional datasets and cutting-edge identification strategies to measure seller performance and identify growth opportunities.<\/p>\n<p><strong>Vivian Yu<\/strong> is an Economist at Amazon, applying cutting-edge causal inference techniques to tackle complex challenges in third-party seller supply chain management. She finds immense fulfillment in seeing her work directly improve outcomes for the company&#8217;s third-party selling partners. Prior to Amazon, she honed her analytical expertise in health care system, financial service, marketing and technology industries, developing a passion for using econometric methods to solve high-impact business problems and deliver tangible value to customers. She has a Bachelor\u2019s degree in Economics from Shanghai Jiao Tong University and a Ph.D. in Economics from State University of New York at Albany.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_slit383 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"lyapunov-approach\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-lyapunov-approach tb_buvp638 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_11ar638 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_uyrm870   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong><em><b><i>Digital Transformation in Transportation Systems: Navigating User Behavior and System Efficiency\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/i><\/b><\/em><\/strong><\/h3>\n<p>This tutorial explores the impact of digitalization on today\u2019s transportation systems, focusing on how emerging information technologies and pricing schemes are reshaping the travel behavior in congestible networks. Our focuses include: static and dynamic routing games, the impact of asymmetric information on network efficiency, and the design of incentives for system efficiency and equity. Through a combination of theoretical insights and empirical studies, the tutorial offers an in-depth analysis of models and tools for analyzing strategic user behavior, the role of information and incentive mechanisms in promoting socially desirable outcomes, and the application of these theories in real-world transportation systems.<\/p>\n<p><strong>Speakers:\u00a0 Haripriya Pulyassary and Manxi Wu<\/strong><\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_wou7295 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-wou7295-0\" class=\"tb_title_accordion\" aria-controls=\"acc-wou7295-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-wou7295-0-content\" data-id=\"acc-wou7295-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong><b>Haripriya Pulyassary<\/b><\/strong> is a PhD student in the School of Operations Research and Information Engineering at Cornell University. Her research focuses on game theory and combinatorial algorithms with applications in transportation systems. Prior to joining Cornell, she obtained her Masters of Mathematics in Combinatorics &amp; Optimization (2022), and Bachelors in Computer Science (2021), from the University of Waterloo.<\/p>\n<p><strong><b>Manxi Wu<\/b><\/strong> is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University. Her research focuses on developing tools in game theory, information design, and market design, with applications in urban systems. Before joining Cornell, she was a Research Fellow at the Simons Institute for the Theory of Computing and a Postdoctoral Researcher in the EECS department at UC Berkeley (2022). Manxi earned her Ph.D. in 2021 from the Institute for Data, Systems, and Society at MIT. She also holds an M.S. in Transportation from MIT and a B.S. in Applied Mathematics from Peking University.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_6907993 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n                        <div  data-anchor=\"incorporating-AI-healthcare\" data-lazy=\"1\" class=\"module_row themify_builder_row tb_has_section tb_section-incorporating-AI-healthcare tb_fd46512 tf_w\">\n                        <div class=\"row_inner col_align_top tb_col_count_2 tf_box tf_rel\">\n                        <div  data-lazy=\"1\" class=\"module_column tb-column col4-3 tb_ot95512 first\">\n                    <!-- module text -->\n<div  class=\"module module-text tb_u2wo92   \" data-lazy=\"1\">\n        <div  class=\"tb_text_wrap\">\n        <h3><strong><em><b><i>An Introduction to Decision Diagrams for Optimization\u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/i><\/b><\/em><\/strong><\/h3>\n<p>This tutorial provides an introduction to the use of decision diagrams for solving discrete optimization problems. A decision diagram is a graphical representation of the solution space, representing decisions sequentially as paths from a root node to a target node. By merging isomorphic subgraphs (or equivalent subproblems), decision diagrams can compactly represent an exponential solution space. This ability can reduce solving time and memory requirements potentially by orders of magnitude. That said, exact decision diagrams can still be of exponential size for a given problem, which limits their practical applicability to relatively small instances. However, recent research has introduced a scalable approach by compiling polynomial-sized relaxed and restricted diagrams that yield dual and primal bounds, respectively. These can be combined in an exact search to produce a generic decision diagram-based branchand-bound method. This chapter describes how this approach provides a scalable solution method for state-based dynamic programming models. In addition, the chapter shows how this approach can be applied to, and embedded in, other computational paradigms including constraint programming, integer programming, and column elimination. After this chapter, readers will have an understanding of the basic principles of decision diagram-based optimization, an appreciation of how it compares it to other optimization methods, and an understanding of what types of optimization problems are most suitable for this new technology.<\/p>\n<p><strong>Speaker:\u00a0<\/strong>Willem-Jan van Hoeve<\/p>    <\/div>\n<\/div>\n<!-- \/module text --><!-- module accordion -->\n<div  class=\"module module-accordion tb_27qh81 \" data-behavior=\"toggle\" data-lazy=\"1\">\n    \n    <ul class=\"ui module-accordion   orange\">\n            <li>\n            <div class=\"accordion-title tf_rel\">\n                <a href=\"#acc-27qh81-0\" class=\"tb_title_accordion\" aria-controls=\"acc-27qh81-0-content\" aria-expanded=\"false\">\n                    <i class=\"accordion-icon\"><svg  class=\"tf_fa tf-ti-plus\" aria-hidden=\"true\"><use href=\"#tf-ti-plus\"><\/use><\/svg><\/i>                                        <span class=\"accordion-title-wrap\">Speaker Bio<\/span>                <\/a>\n            <\/div><!-- .accordion-title -->\n            <div id=\"acc-27qh81-0-content\" data-id=\"acc-27qh81-0\" aria-hidden=\"true\" class=\"accordion-content tf_hide tf_clearfix\">\n                                    <div class=\"tb_text_wrap\">\n                        <p><strong>Willem-Jan van Hoeve<\/strong> is the Carnegie Bosch Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University. His research focuses on developing new methodologies for mathematical optimization with applications to network design, scheduling, vehicle routing, data mining, and others. He made notable contributions to the areas of constraint and integer programming, and most recently pioneered the field of decision diagrams for optimization. Van Hoeve\u2019s research has been funded by the National Science Foundation, the Office of Naval Research, and two Google Faculty Research Awards. He has consulted for a variety of companies including FedEx Ground, Exxon Mobil, PNC Bank, Bosch\/Siemens, and Charter Steel, as well as a number of non-profit organizations. Van Hoeve is the recipient of the INFORMS Computing Society Harvey J. Greenberg Research Award, the Tepper School&#8217;s MBA Teaching Award (twice) and MSBA Teaching Award, and several best paper awards. He is currently Associate Editor for the INFORMS Journal on Computing and Editor of the journal Artificial Intelligence.<\/p>                    <\/div>\n                            <\/div><!-- .accordion-content -->\n        <\/li>\n        <\/ul>\n\n<\/div><!-- \/module accordion -->        <\/div>\n                    <div  data-lazy=\"1\" class=\"module_column tb-column col4-1 tb_85lx761 last\">\n                            <\/div>\n                        <\/div>\n        <\/div>\n        <\/div>\n<!--\/themify_builder_content-->","protected":false},"excerpt":{"rendered":"<p>The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new [&hellip;]<\/p>\n","protected":false},"author":46,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"class_list":["post-205","page","type-page","status-publish","hentry","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.0) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>TutORials - 2024 INFORMS Annual Meeting<\/title>\n<meta name=\"description\" content=\"The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science.\" \/>\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\/seattle2024\/tutorials\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TutORials\" \/>\n<meta property=\"og:description\" content=\"The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/\" \/>\n<meta property=\"og:site_name\" content=\"2024 INFORMS Annual Meeting\" \/>\n<meta property=\"article:modified_time\" content=\"2024-09-23T13:43:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/files\/2023\/10\/twitter-img.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1400\" \/>\n\t<meta property=\"og:image:height\" content=\"626\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\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\/seattle2024\/tutorials\/\",\"url\":\"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/\",\"name\":\"TutORials - 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2024 INFORMS Annual Meeting","description":"The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science.","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\/seattle2024\/tutorials\/","og_locale":"en_US","og_type":"article","og_title":"TutORials","og_description":"The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science.","og_url":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/","og_site_name":"2024 INFORMS Annual Meeting","article_modified_time":"2024-09-23T13:43:56+00:00","og_image":[{"width":1400,"height":626,"url":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/files\/2023\/10\/twitter-img.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/","url":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/","name":"TutORials - 2024 INFORMS Annual Meeting","isPartOf":{"@id":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/#website"},"datePublished":"2024-07-14T19:51:00+00:00","dateModified":"2024-09-23T13:43:56+00:00","description":"The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science.","breadcrumb":{"@id":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/tutorials\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/"},{"@type":"ListItem","position":2,"name":"TutORials"}]},{"@type":"WebSite","@id":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/#website","url":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/","name":"2024 INFORMS Annual Meeting","description":"October 20-23, 2024 | Seattle, WA","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"builder_content":"<p>The <em>TutORials in Operations Research<\/em> series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meeting.<\/p>\n<h3><strong>Nanoretail Operations in Developing Markets<\/strong><\/h3> <p>Across much of the developing world, family-operated nanostores provide daily grocery needs to billions of poorly paid consumers. This highly fragmented retail channel is of critical importance to consumer brands as in many markets this is the largest retail channel. We characterize the empirical context in which these stores operate, as well as the intricate operations that manufacturers and distributors put in place to supply them with their goods. We then elaborate on modeling operations execution and operations strategies to expose critical tradeoffs that are distinct from those in organized retail in developed markets. We discuss research results that have demonstrated why many manufacturers choose to serve this market directly and at high frequency, why manufacturers invest considerable effort in deploying sales agents networks, how this channel manages to remain competitive with modern organized retail such as convenience store chains, and how digitization and novel financing solutions provide a further competitive advantage to the nanoretail channel. Finally, we discuss how to conduct research in this retail segment and provide examples of novel contexts and business models that may open up new areas of nanoretail research.<\/p> <p><strong>Speakers: Jan C. Fransoo, Rafael Escamilla, and Jiwen Ge<\/strong><\/p>\n<ul><li><h4>Speaker Bios<\/h4><p><strong>Jan C. Fransoo<\/strong> is Professor of Operations and Logistics Management in the Department of Information Systems &amp; Operations Management at Tilburg University, and holds courtesy affiliations at Eindhoven University of Technology and Massachusetts Institute of Technology. Fransoo has over 30 years of experience in conducting research in operations and supply chain management, making use of a wide variety of analytical, quantitative, and qualitative methods. His research is widely published in and cited by the top operations management and other high-impact academic journals, and in several of his (edited) books, notably \u201cSustainable Supply Chain Management\u201d, \u201cReaching 50 Million Nanostores\u201d, and \u201cBehavioral Operations in Planning and Scheduling\u201d. His current research interests focus on retail operations in developing markets, and on the role of humans in AI-enhanced decision making. <br>Fransoo holds an MSc in Industrial Engineering and a PhD in Operations Management from Eindhoven University of Technology.<\/p> <p><strong><b>Rafael Escamilla\u00a0<\/b><\/strong>holds the position of Assistant Professor in Supply Chain Management at the W.P. Carey School of Business at Arizona State University. He specializes in retail supply chains within emerging markets, employing field experiments and econometric techniques to investigate the factors that impact supply chain decision-making. Rafael\u2019s academic background includes a joint PhD degree from Tilburg University and Kuehne Logistics University, a Graduate Certificate in Logistics and Supply Chain Management from MIT, a Master of Science from the Universit\u00e9 de Technologie de Troyes, and a Bachelor of Engineering from Tecnol\u00f3gico de Monterrey. Rafael has taught courses on topics such as Logistics, Causal Inference, Supply Chain Strategy, Data Science, Research Methods, and Project Management. His work has received support from industry collaborations, and he has conducted research as a visiting scholar at the Wharton School, University of Pennsylvania.<\/p> <p><strong><b>Jiwen Ge<\/b><\/strong> is an associate professor at the Institute of Supply Chain Analytics, Dongbei University of Finance and Economics. Previously, he was a post-doctoral research fellow at the Tuck School of Business, Dartmouth College, and completed his PhD at Eindhoven University of Technology in the Netherlands. His research focuses on emerging-market retail, examining operations in traditional nanostore channels, modern organized offline channels, and e-commerce. He employs both analytical modeling and empirical methodologies in his work.<\/p><\/li><\/ul>\n<h3>Humanitarian Operations and Earmarked Funding<\/h3> <p>Earmarked funding, also referred to as restricted funding, is one of the main characteristics of humanitarian operations. Earmarked funding can be defined as the donors\u2019 contributions to humanitarian organizations to be used for a specific purpose. This is in contrast to flexible funding, which can be used for any purpose. This tutorial introduces the trade-off between total donations and operational performance due to humanitarian earmarking. The tutorial explains why allowing donors to earmark their contributions helps organizations increase fundraising effectiveness. It also explains why earmarking hurts organizations\u2019 per-dollar performance. Because humanitarian organizations\u2019 utility increases in total donations and per-dollar (or any other currency) performance, the best fundraising strategy for the organizations, collecting earmarked or flexible funds, is not apparent. Moreover, this tutorial argues that earmarked funding is here to stay and discusses models that reduce the negative effect of earmarked funding. Then, it proposes that a thoughtful mix of earmarked and flexible donations may be the best way for humanitarian organizations to fund their operations. The balance between earmarked and flexible funds depends on reducing the negative operational consequences of earmarked funds. The tutorial concludes by identifying areas for future research on humanitarian operations and earmarked funding.<\/p> <p><strong>Speaker: Alfonso J. Pedraza-Martinez<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Alfonso Pedraza-Martinez, PhD<\/b><\/strong> (INSEAD, 2011), directs the HOPE - Humanitarian Operations Lab at the University of Notre Dame. His research has informed international humanitarian organizations and faith-based relief organizations. He teaches humanitarian operations analytics at the MBA and PhD levels and enjoys directing undergraduate research projects. Alfonso received the 2022 Luk Van Wassenhove Career Award from the European Working Group on Humanitarian Operations. He has edited special issues in Production and Operations Management, the Journal of Operations Management, and the European Journal of Operational Research. Professor Pedraza-Martinez is a former president of the College of Humanitarian Operations and Crisis Management at the Production and Operations Management Society.<\/p><\/li><\/ul>\n<h3>Nonconvex, Nonsmooth, and Nonregular Optimization: A Computational Framework 9299<\/h3> <p>Algebraic modeling languages presently lack the ability to effectively support the<br>formulation and solution of nonconvex and nonsmooth optimization problems. Since<br>an arbitrary problem of this kind is intractable, any hope to achieve practically useful<br>solutions would rely on means to convey specific problem structure to an algorithm.<br>In this tutorial, we present a framework for specifying nonconvex, nonsmooth, and<br>nonregular problems within an algebraic modeling language that makes available the<br>key structural properties to an algorithm. It also facilitates experimentation with<br>different model formulations and algorithmic approaches. The framework entails a<br>change of mindset away from the traditional formulation of objective and constraint<br>functions, and instead asks the analyst to specify a basic feasible set, a basic objective<br>function, one or more monitoring functions, and several performance functions. Eleven<br>examples ranging from goal programming to variational inequalities and engineering<br>risk analysis illustrate the practical implications of the framework. <\/p> <p><strong>Speaker:\u00a0<\/strong><strong>Michael C. Ferris, Olivier Huber, and Johannes O. Royset<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Michael C. Ferris<\/b><\/strong> holds the John P. Morgridge Chair in Computer Sciences, and is the Jacques-Louis Lions Professor of Computer Sciences at the University of Wisconsin, Madison, USA. He is the Director of the Data Sciences Hub within the Wisconsin Institutes for Discovery.\u00a0 He received his PhD from the University of Cambridge, England in 1989.Dr. Ferris' research is concerned with algorithmic and interface development for large scale problems in mathematical programming, including links to the GAMS and AMPL modeling languages, and general purpose software such as PATH, NLPEC and EMP.\u00a0 He has worked on many applications of both optimization and complementarity, including covid vaccine delivery, cancer treatment planning, energy modeling, economic policy, traffic and environmental engineering, video-on-demand data delivery, structural and mechanical engineering. Ferris is a SIAM fellow, an INFORMS fellow,\u00a0received the Beale-Orchard-Hays prize from the Mathematical Programming Society and is a past recipient of a NSF Presidential Young Investigator Award, and a Guggenheim Fellowship.\u00a0 He serves\u00a0on the editorial boards of Informs Journal on Computing\u00a0 and Optimization Methods and Software.<\/p> <p><strong>Dr. Olivier Huber<\/strong> is a staff scientist at the University of Wisconsin, Madison, USA. <br>He completed his PhD at INRIA Grenoble, France in 2015. <br>His research interests are in the development of modeling and algorithmic frameworks for complex optimization problems.<\/p> <p><strong><b>Dr. Johannes O. Royset<\/b><\/strong> is a professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at University of Southern California. He was awarded a Young Investigator Award from the Air Force Office of Scientific Research in 2007, and the Barchi Prize as well as the MOR Journal Award from the Military Operations Research Society in 2009. He received the Carl E. and Jessie W. Menneken Faculty Award for Excellence in Scientific Research in 2010 and the Goodeve Medal from the Operational Research Society in 2019. Professor Royset was a plenary speaker at the International Conference on Stochastic Programming in 2016, the SIAM Conference on Uncertainty Quantification in 2018, and the INFORMS Security Conference in 2022. He has a Doctor of Philosophy degree from the University of California at Berkeley (2002). Professor Royset has been an associate or guest editor of SIAM Journal on Optimization, Operations Research, Mathematical Programming, Journal of Optimization Theory and Applications, Naval Research Logistics, Journal of Convex Analysis, Set-Valued and Variational Analysis, and Computational Optimization and Applications. He has published two books and more than 100 articles.<\/p><\/li><\/ul>\n<h3><strong>Experimental Design for causal Inference Through an Optimization Lens<\/strong><\/h3> <p>AThe study of experimental design offers tremendous benefits for answering causal ques tions across a wide range of applications, including agricultural experiments, clinical trials, industrial experiments, social experiments, and digital experiments. While valu able in such applications, the costs of experiments often drive experimenters to seek more efficient designs. Recently, experimenters have started to examine such efficiency questions from an optimization perspective, as experimental design problems are fun damentally decision-making problems. This perspective offers a lot of flexibility in leveraging various existing optimization tools to study experimental design problems. thus aims to examine the foundations of experimental design problems in the context of causal inference as viewed through an optimization lens.<\/p> <p><strong>Speaker: Jinglong Zhao<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong>Jinglong Zhao<\/strong> is an Assistant Professor of Operations and Technology Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology.<\/p><\/li><\/ul>\n<h3>Machine Learning Methods for Large Population Games<\/h3> <p>In this tutorial, we provide an introduction to machine learning methods for finding Nash equilibria in games with large number of agents. These types of problems are important for the operations research community because of their applicability to real life situations such as control of epidemics, optimal decisions in financial markets, electricity grid management, or traffic control for self-driving cars. We start the tutorial by introducing stochastic optimal control problems for a single agent, in discrete time and in continuous time. Then, we present the framework of dynamic games with finite number of agents. To tackle games with a very large number of agents, we discuss the paradigm of mean field games, which provides an efficient way to compute approximate Nash equilibria. Based on this approach, we discuss machine learning algorithms for such problems. First in the context of discrete time games, we introduce fixed point based methods and related methods based on reinforcement learning. Second, we discuss machine learning methods that are specific to continuous time problems, by building on optimality conditions phrased in terms of stochastic or partial differential equations. Several examples and numerical illustrations are provided along the way.<\/p> <p><strong>Speakers: G\u00f6k\u00e7e Dayanikli and Mathieu Lauri\u00e8re<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>G\u00f6k\u00e7e Dayan\u0131kl\u0131<\/b><\/strong> is an Assistant Professor of Statistics at the University of Illinois Urbana-Champaign where she is also an Affiliate Faculty in the Department of Industrial &amp; Enterprise Systems Engineering. Before joining UIUC, she worked as a Term Assistant Professor at Columbia University, Department of Statistics. She completed her Ph.D. in Operations Research &amp; Financial Engineering at Princeton University where she was the recipient of School of Engineering and Applied Science Award for Excellence. During Fall 2021, she was a visiting graduate researcher at the Institute for Mathematical and Statistical Innovation (IMSI) to participate in the Distributed Solutions to Complex Societal Problems program.<\/p> <p>\u00a0<strong><b>Mathieu Lauri\u00e8re<\/b><\/strong> is an Assistant Professor of Mathematics and Data Science at NYU Shanghai. Prior to joining NYU Shanghai, he was a Postdoctoral Research Associate at Princeton University in the Operations Research and Financial Engineering (ORFE) department. He obtained his MS from Sorbonne University and ENS Paris-Saclay and his PhD from the University of Paris. Before joining Princeton University, he was a Postdoctoral Fellow at the NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai. Most recently, Mathieu was a Visiting Faculty Researcher at Google Brain, for the Brain Team (Paris).<\/p><\/li><\/ul>\n<h3>Food Bank Operations: A U.S. Perspective on Humanitarian Food Assistance<\/h3> <p>Food banks are non-profit organizations with the primary mission of providing food assistance to the communities they serve. This assistance occurs through the collection, storage, and distribution of food to people in need through a complex supply chain network of donors and charitable agencies. Distribution of food is challenging\u00a0in this environment, given the many resource constraints experienced at the financial, human, and material levels. Furthermore, the COVID-19 pandemic highlighted\u00a0the important role these organizations play as demand for food assistance surged,\u00a0while sources of supply were constrained. This tutorial provides an overview of food\u00a0bank operations from a supply chain perspective. We specifically characterize the key\u00a0stakeholders, product, and information flows within the food bank supply chain and\u00a0draw from our prior experience with several U.S. food banks to delineate structural\u00a0differences that exist among these supply chain networks. We further elucidate the\u00a0influence of the supply chain network design on the organizations\u2019 operational decisions and strategic direction with respect to equity, efficiency, effectiveness, diversity\u00a0and inclusion. We present several studies that illustrate the role of descriptive, predictive, and prescriptive analytics in improving the distribution of food and reducing\u00a0food waste, as well as provide insights for future research in this area.<\/p> <p><strong>Speakers:\u00a0Lauren Davis, Irem Sengul Orgut, Steven Jiang, Eric Aft, Charlie Hale, Larry Morris, Jean Rykaczewski<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Lauren Davis<\/b><\/strong> is a professor in the Department of Industrial and Systems Engineering at North Carolina A&amp;T (NCAT). She received her Ph.D. in Industrial Engineering from North Carolina State University.\u00a0 Prior to joining NCA&amp;T she spent 12 years as a senior software engineer at IBM\u00a0 supporting SAP implementations in Research Triangle Park (NC), Guadalajara (Mexico) and Shenzen (China). Her research focuses on stochastic modeling of supply chain systems in for-profit and non-profit settings, with application areas in hunger relief, humanitarian logistics, and homeland security. Her work has appeared several peer reviewed journal publications including European Journal of Operational Research, International Journal of Production Economics and the Journal of Humanitarian Logistics and Supply Chain Management. Her work has been supported by the National Science Foundation, Department of Homeland Security, and US Department of Agriculture.\u00a0 Dr. Davis is an active member of INFORMS and has served in various roles including the President of the Minority Issues Forum, chair of volunteer service award committee, and chair of the AMAZON SCOT Scholarship committee. She currently serves as an associate editor for Transportation Science and serves s as a board member of the Second Harvest Food Bank of Northwest North Carolina.<\/p> <p><strong><b>Dr. Irem Sengul Orgut<\/b><\/strong> is an Assistant Professor of Operations Management and an Assistant Director at the Institute of Data and Analytics (IDA) at Culverhouse College of Business at the University of Alabama. She received her Ph.D. in Industrial Engineering with a minor in Statistics at North Carolina State University, Raleigh, NC in 2015. Prior to joining UA, she worked as Analytics Project Manager at Lenovo, Morrisville, NC. Her research focuses on using analytics and operations research methods to address problems involving multiple decision-makers with conflicting objectives and various sources of uncertainty in humanitarian operations management with an emphasis on hunger relief operations. In her role at IDA, she has founded and directs the UPWARD (Underserved Populations Workgroup for Analytics Research and Development) Initiative that uses data and analytics methods to solve unique challenges humanitarian and nonprofit organizations face and alleviate the well-being of underserved populations. Dr. Sengul Orgut\u2019s research has appeared in journals including the <em><i>European Journal of Operational Research<\/i><\/em> and <em><i>Production and Operations Management<\/i><\/em>, and it has been supported by the National Science Foundation. Dr. Sengul Orgut received the University of Alabama's Excellence in Community-Engaged Scholarship Award for her work in the UPWARD initiative. She serves as the secretary of the Women in OR\/MS (WORMS) forum at INFORMS and is on the editorial review board of the <em><i>Decision Sciences Journal<\/i><\/em>.<\/p> <p><strong><b>Steven Jiang<\/b><\/strong> is an Associate Professor in the Department of Industrial and Systems Engineering at North Carolina Agricultural and Technical State University. He received his PhD in Industrial Engineering from Clemson University in 2001. His research interests include human systems integration, supply chain, visual analytics, and cognitive engineering.<\/p> <p>He has authored or coauthored more than 150 technical publications in refereed journals and refereed conference proceedings. Dr. Jiang has received research funding from the National Science Foundation, US Army research laboratory, US Department of Transportation, US Airforce Laboratory, North Carolina Department of Transportation, and US Department of Homeland Security. Dr. Jiang is on the Editorial Board of the International Journal of Industrial Ergonomics.<\/p> <p><strong><b>Eric A. Aft <\/b><\/strong>has over 34 years in nonprofit leadership roles, Eric feels privileged to lead Second Harvest Food Bank of Northwest North Carolina as its third CEO.\u00a0 He has been in this role since 2018.\u00a0During Eric\u2019s six-year tenure at SHFBNWNC, he has focused the organization on integrating immediate response activities with root-cause initiatives, including enhanced partnerships with health systems, colleges, school systems, expanded workforce training, and heightened the focus on nutrition education and outreach along with creative food distribution strategies. In addition, he has overseen the completion of a $25 million new headquarters building with no debt and the organization\u2019s first-ever satellite location. Eric also established a more equitable wage structure within the organization and enhanced growth opportunities for team members.\u00a0Recent organizational and personal recognitions include:<\/p> <ul> <li>Winner of Amazon Web Services IMAGINE Grant Go Further, Faster Award (2023\/2024)<\/li> <li>Green Business of the Year by the Piedmont Environmental Alliance (2023)<\/li> <li>Triad Business Journal\u2019s Leaders in Diversity Award (2023)<\/li> <li>Runner-up for Best Commercial Real Estate (Industrial) Projects (2022)<\/li> <li>Triad Business Journal\u2019s C-Suite Award Winner (2020)\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/li> <\/ul> <p>Prior to joining Second Harvest, Eric worked in the United Way system for 26 years, including 11 year as chief executive officer for two United Way organizations \u2013 one in Virginia and one in Ohio. He also served briefly as a member of the Wake Forest University Advancement team.\u00a0 Eric also appreciates the opportunity to serve for the past seven years as an adjunct faculty member at Salem College.\u00a0He is a graduate of Rhodes College in Memphis and holds a Master\u2019s degree from the University of South Carolina in Health Administration.\u00a0<\/p> <p><strong><b>Charlie Hale<\/b><\/strong> joined the Food Bank of Central &amp; Eastern North Carolina in March 2005 as the IT Director and became Vice President of IT &amp; Operations in 2007.\u00a0 Charlie graduated from NC State University in 1989 with a Bachelor of Science degree in Meteorology.\u00a0 Prior to joining the Food Bank Charlie as the IT Director at Smithfield Premium Genetics. Since joining the Food Bank, Charlie has worked on many projects to enhance the organization including infrastructure expansion, growth in equitable food distribution, raising the bar on food safety, and disaster relief operations.\u00a0 Charlie\u2019s current role with the Food Bank is Chief Operating Officer which includes the areas of facilities maintenance, transportation, food safety &amp; warehouse compliance, distribution &amp; inventory control, child and senior nutrition programs, and food sourcing &amp; network services.<\/p> <p><strong><b>Larry Morris<\/b><\/strong> has served as the Director of Network Engagement at the Food Bank of Central &amp; Eastern North Carolina. He has worked with the Food Bank since 1993.\u00a0He is the Food Bank\u2019s technical expert for agency compliance issues related to Food Bank and Feeding America policies. He leads a team that is responsible for ensuring agency engagement and compliance for nearly 650 partner agencies in a 34-county service area. He also established the Food Bank\u2019s Partner Agency Advisory Council in 2017.\u00a0Larry has extensive experience in Food Bank disaster relief efforts in response to hurricanes, ice storms, tornadoes, and the COVID-19 pandemic.\u00a0Larry also has served on Feeding America\u2019s Agency Relations Council and serves on the local Emergency Food &amp; Shelter Board for Wake County. He helped to coordinate the Food Bank\u2019s participation in five national hunger research studies from 1993 through 2013. He has presented numerous times at Feeding America conferences; sharing information and experiences from the Food Bank of Central &amp; Eastern North Carolina with other agency relations professionals from food banks throughout the U.S.\u00a0In his spare time, Larry enjoys spending quality time with his family, working out, gardening and reading anything related to astronomy.<\/p> <p><strong>Jean Rykaczewski <\/strong>joined the West Alabama food Bank in 2016 as Executive Director and becoming CEO in 2021. Jean graduated Indiana University of PA and UA. Prior to joining the Food Bank Jean has taken a failing Food Bank, to a successful Food Bank that was debt free. In 2023 moved the Food Bank to a new location doubling the square footage, and building a 540 pallet freezer and cooler. Before and including the move Jean restructured systems and operations, expanded the growth in equitable food distribution especially throughout the Black Belt where poverty rates are 37-40% , and improved disaster relief operations. Jean will be introducing a teaching kitchen and Food Pharmacy opening this fall.<\/p><\/li><\/ul>\n<h3>Combining Large Language Models and OR\/MS to Make Smarter Decisions<\/h3> <p>Operations Research\/Management Science (OR\/MS) capabilities can provide tremendous value in helping enterprises and individuals make smarter decisions. However, the creation and deployment of OR\/MS-based decision-making solutions require significant time and expertise, making their widespread application challenging. Large language models (LLMs), exemplified by models such as ChatGPT, Gemini, and Claude, are deep neural network models encompassing billions to trillions of parameters. These models are pre-trained with a vast scope of general knowledge and are quickly adaptable to many downstream tasks. Beyond core capabilities like document summarization and code generation, LLMs exhibit emerging capabilities, such as learning new tasks from a few natural language examples. Generative AI technologies like LLMs, therefore, have transformative potential for many fields and professions. In this work, we explore the potential of LLMs and their capabilities to significantly improve the creation of OR\/MS based decision-making solutions. After providing relevant technical background on LLMs, we show this potential through three concrete and detailed examples: using LLMs to significantly reduce the time required to create decision-making applications while improving their quality; using LLMs to extract structured information from unstructured text without the need to create new natural language processing models, a capability important, for example, for demand forecasting; and using LLMs to drive natural language-based interfaces enabling a business user to easily and flexibly interact with analytical models used for decision making. As LLMs are a new technology that introduces new risks, this work also provides guidelines for their productive, ethical and responsible use and describes ongoing developments relevant to the OR\/MS profession. We hope this work will serve as a starting point both for the application of LLMs to the OR\/MS use cases we described and as the starting point for exploring new and exciting integrations between LLMs and OR\/MS, ultimately enabling the use of OR\/MS to make smarter decisions in a much more widespread manner.<\/p> <p><strong>Speakers:<\/strong><\/p> <p><strong>Segev Wasserkrug, Leonard David Jean Boussioux, and Wei Sun<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Segev Wasserkrug<\/b><\/strong> is a Senior Technical Staff Member and technical leader at IBM Research and holds a Research Fellow position at the Technion's Faculty of Data and Decision Sciences. He is an expert in optimization, game theory, and machine learning, with a current focus on enhancing decision-making by integrating Foundation Models and Large Language Models with Operations Research and Management Science.Segev has over two decades of experience in utilizing advanced analytics, AI, and optimization across various domains to help IBM and its clients make better decisions. Segev holds a strong academic record in operations research, computer science and AI, with over 50 academic publications and 45 patent submissions. He is the winner of the DEBS 2018 test of time award, a three-time INFORMS Wagner Prize finalist and was a finalist for the 2018 INFORMS Innovative Application in Analytics Award. Segev received his Ph.D. in Information Systems Engineering, and also holds M.Sc. and B.A. degrees in Computer Science from the Technion.<\/p> <p><strong><b>L\u00e9onard Boussioux<\/b><\/strong> is an assistant professor in Information Systems and Operations Management at the University of Washington's Foster School of Business, with an adjunct position at the Allen School of Computer Science &amp; Engineering. He holds a PhD in Operations Research from MIT and focuses his research on applying analytics and artificial intelligence (AI) to healthcare, climate, sustainability, and societal improvement. Additionally, L\u00e9onard is an affiliated faculty at the Laboratory for Innovation Science at Harvard, where he explores creative problem-solving and AI's impact on the future of work and innovation.\u00a0His research and entrepreneurial work have received wide recognition, including giving two TEDx talks, winning the MIT 3-minute thesis competition, and awards from INFORMS, MIT, IEEE, UNESCO, and Google. He previously worked at Google X, Mila, UC Berkeley, and the French National Centre for Scientific Research. L\u00e9onard is also a passionate teacher and has received five teaching awards at the University of Washington and MIT, including the Goodwin Medal.<\/p> <p><strong><b>\u00a0<\/b><\/strong><strong><b>Wei Sun<\/b><\/strong> is a senior research scientist at IBM T. J. Watson Research Center in NY. Her research focuses on the intersection of machine learning and optimization, encompassing causal decision-making, constrained predictions, and game theory. Her work has been instrumental in addressing real-world challenges for companies in digital marketing, travel\/transportation, and financial services.\u00a0Wei holds a Ph.D. in operations research and an SM in computational design and optimization from MIT. Additionally, she has an SM in computational engineering and a B.Eng. in electrical and computer engineering with First-Class Honors from the National University of Singapore.<\/p><\/li><\/ul>\n<h3>Interventions for Patients with Complex Medical and Social Needs<\/h3> <p>Patients with multiple chronic conditions and social needs represent a small percentage of the population but have a disproportionate impact on healthcare costs and utilization. Organizations around the United States have created programs \u2013 often referred to as complex care interventions \u2013 to improve the health and well-being of such patients and reduce avoidable hospital and emergency department use. In this tutorial, we focus on two emerging themes in the field: (1) identifying clinically meaningful subgroups in complex care populations through unsupervised learning methods; and (2) describing the key operational features of interventions with an emphasis on staffing needs and the impact on patient outcomes. The material presented in this tutorial draws on the research of the Healthcare Operations Research Lab at the University of Massachusetts, Amherst, and its collaborating partners. To illustrate these themes and contextualize the details of complex care delivery, we use a range of patient-level examples, visualizations, descriptive summaries, case studies, and results from the clinical literature.<\/p> <p><strong>Speakers: Hari Balasubramanian, Sindhoora Prakash, Ali Jafari, Arjun Mohan, and Chaitra Gopalappa<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Dr. Hari Balasubramanian<\/b><\/strong> is Professor of Industrial Engineering at the University of Massachusetts, Amherst. His research utilizes methods in optimization, simulation, stochastic modeling, and statistical learning to improve healthcare delivery. Past projects have included capacity planning and scheduling in outpatient, inpatient, and surgical care. His recent work is on detecting population level patterns in the prevalence of multiple chronic conditions and evaluating interventions. Dr. Balasubramanian's research has been funded by multiple grants from the National Science Foundation (NSF), including an NSF CAREER award (2013-2019) focused on improving primary care delivery.<\/p> <p><strong><b>Sindhoora Prakash<\/b><\/strong> is a PhD student in the Industrial Engineering and Operations Research program at the University of Massachusetts, Amherst. Her current research, funded by the NSF, focuses on analyzing and optimizing intervention delivery for patients with complex medical and social needs. She holds a Bachelor of Technology in Information Technology from the National Institute of Technology Karnataka, Surathkal. Sindhoora has also worked as a Data Scientist at Tesco, where she led operations research initiatives to solve problems in the retail domain, such as markdown pricing, space optimization, and floor planning.<\/p> <p><strong><b>Ali Jafari<\/b><\/strong> is a PhD student in the Industrial Engineering and Operations Research program at the University of Massachusetts, Amherst. His current research focuses on healthcare, specifically analyzing the connections between various diseases and disease groups to identify previously unknown relationships. Ali holds a Bachelor's degree in Electrical Engineering from K.N. Toosi University of Technology in Tehran, Iran. His growing interest in operations research led him to complete a Master's degree in Industrial Engineering from the University of Tehran, Iran, and pursue doctoral studies in the field.<\/p> <p><strong><b>Arjun Mohan<\/b><\/strong> currently works as a Supply Chain Analyst at AFI Furnishings. He holds a Master\u2019s in Industrial Engineering and Operations Research from the University of Massachusetts, Amherst. He also has a Bachelor's in Chemical Engineering from Mumbai University, India. His research focuses on multimorbidity, specifically the identification of complex combinations of conditions and patterns in comorbidities. He has previously worked as a Data Scientist at CRISIL, Mumbai, and EXL Services Inc., Jersey City. His work has primarily involved the redesign and optimization of operational procedures, and the design of business processes using predictive modeling and decision analytics.<\/p> <p><strong><b>Dr. Chaitra Gopalappa<\/b><\/strong> is an associate professor of industrial engineering at the Department of Mechanical and Industrial Engineering and Commonwealth Honors College at the University of Massachusetts, Amherst. She is also a guest researcher at the U.S. Centers for Disease Control and Prevention. Her research is in areas of data analytics, predictive modeling, and control optimization applied to health and social equity research. Her lab is funded by grants from the National Institutes of Health, the National Science Foundation, the U.S. Centers for Disease Control and Prevention, and the World Health Organization.<\/p><\/li><\/ul>\n<h3>Randomized Rounding Approaches to Online Allocation, Sequencing, and Matching<\/h3> <p>Randomized rounding is a technique that was originally used to approximate hard offline discrete optimization problems from a mathematical programming relaxation. Since then it has also been used to approximately solve sequential stochastic optimization problems, overcoming the curse of dimensionality. To elaborate, one first writes a tractable linear programming relaxation that prescribes probabilities with which actions should be taken. Rounding then designs a (randomized) online policy that approximately preserves all of these probabilities, with the challenge being that the online policy faces hard constraints, whereas the prescribed probabilities only have to satisfy these constraints in expectation. Moreover, unlike classical randomized rounding for offline problems, the online policy\u2019s actions unfold sequentially over time, interspersed by uncontrollable stochastic realizations that affect the feasibility of future actions. This tutorial provides an introduction for using randomized rounding to design online policies,<\/p> <p><strong>Speaker: Will Ma<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Will Ma<\/b><\/strong> is the Roderick H. Cushman Associate Professor of Business at the Graduate School of Business, Columbia University. His research centers around online algorithms in e-commerce systems, both for supply-side problems like inventory and fulfillment, and revenue management problems like dynamic assortment optimization. He specializes in designing simple online algorithms with performance guarantees, that can be tuned to historical data. Will also has miscellaneous experience as a professional poker player, video-game startup founder, and karaoke bar pianist.<\/p><\/li><\/ul>\n<h3>Measuring the Efficacy of Amazon\u2019s Recommendation Systems<\/h3> <p>Amazon\u2019s Fulfillment By Amazon (FBA) program provides assistance to Selling Partners (\u201csellers,\u201d for short) in the form of information sharing, recommendations guiding seller actions (e.g., restock quantity recommendations, excess inventory recommendations), and delegated actions (e.g., automated removals of aged inventory). Amazon\u2019s vision is to help sellers make better decisions and achieve better business outcomes.<br>In this tutorial, we consider the sophisticated optimization models Amazon employs to generate recommendations. For example, if a seller has excess inventory, Amazon recommends actions to increase their sell-through rate, such as creating a sale or Sponsored Product ad. We demonstrate how we measure the efficacy of these recommendation systems on seller-product outcomes (e.g., revenue, units shipped, and customer clicks on product listings, or \u201cglance views\u201d). Measuring such outcomes is a causal inference problem because we only observe each seller-product\u2019s \u201cfactual\u201d and not their \u201ccounterfactual\u201d outcome. We employ causal machine learning methodologies such as double machine learning, causal forest, and doubly-robust forest to separate selection bias from a comparison of \u201ctreatment\u201d and \u201ccontrol\u201d sellers. For example, we find that aligning with the restock and excess inventory recommendations, on average, improves several seller-salient outcomes. We also present methods for measuring heterogeneity in the efficacy of these recommendations across seller and product segments, and estimate personalized benefits for each seller-product. Finally, through A\/B testing, we find that sharing quantified efficacy information with sellers increases their adoption of Amazon recommendations. Sellers are responding to this messaging, and the duty to them is to rigorously identify causal estimates.<\/p> <p><strong>Speakers:<\/strong><\/p> <p><strong>Ozalp Ozer, Serdar Simsek, Xiaoxi Zhao, Ethan Dee, and Vivian Yu<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong>\u00d6zalp \u00d6zer<\/strong> is an executive leader at Amazon, where he oversees the development of science to optimize Amazon's core supply chain. Leading a team of product managers and scientists\u2014including operations researchers, economists, data scientists, and ML experts\u2014he develops optimization algorithms and predictive models that drive the Fulfillment-By-Amazon (FBA) business and also the recently announced Supply Chain by Amazon. His work spans key areas such as seller recommendation systems (e.g., replenishment, advertising, pricing), resource management systems (e.g., inbound and capacity management), and voice of seller systems (e.g., experimentation, sentiment analysis). Dr. \u00d6zer is also a chaired Professor of Management Science, with prior faculty positions at the UTD, MIT, Columbia University, and Stanford University. His expertise includes global value chain management, entrepreneurship, innovation, strategic investment, capacity and inventory planning, market timing, distribution channel management, procurement contract design, and retail and pricing management. He has published extensively in top journals, using tools from Operations Research, Economics, Statistics, Machine Learning, and Management Science. He has received numerous prestigious awards, including the George and Fonsa Brody Professorship, Ashbel Smith Professorship, Wickham Skinner Early-Career Research Accomplishment Award, Hellman Faculty Fellowship, and Terman Faculty Fellowship. Recognized as a Favorite Professor by Poets &amp; Quants, he has earned teaching awards from MIT, Columbia, and Stanford. He is the editor of The Oxford Handbook of Pricing Management and currently serves as an associate editor for <em>Management Science, M&amp;SOM, Operations Research<\/em>, and <em>Production and Operations Management<\/em>. Dr. \u00d6zer\u2019s industry consulting experience includes work with General Motors, Hewlett Packard, Hitachi GST, IBM, Neiman Marcus, and University Hospitals of Cleveland. He holds Ph.D. and M.S. degrees from Columbia University.<\/p> <p>\u00a0<strong>Serdar \u015eim\u015fek<\/strong> is an Associate Professor of Operations Management at the Jindal School of Management, University of Texas at Dallas, and an Amazon Visiting Academic. His research focuses on pricing, revenue management, and supply chain management problems, primarily in the retail industry and business-to-business markets. His methodological expertise spans various empirical methods (e.g., causal inference, structural estimation, Bayesian estimation, machine learning), economic theories, and Operations Research tools. He extensively collaborates with companies and leverages their data to develop practical and impactful solutions. His work has been published in leading journals such as <em>Management Science, M&amp;SOM<\/em>, <em>Operations Research, <\/em>and <em>Production and Operations Management<\/em>. At Amazon, Dr. \u015eim\u015fek works within the Fulfillment-By-Amazon (FBA) Science team, collaborating with scientists and product managers from various teams across the company. He has significantly contributed to several projects aimed at measuring and enhancing the efficacy of assistance Amazon provides to third-party sellers. Additionally, he works on developing science-driven products in areas such as capacity planning, procurement, and pricing\/promotion management. He received his B.S. degree from Bilkent University, and Ph.D. and M.Phil degrees from Columbia University's Graduate School of Business.<\/p> <p>\u00a0<strong>Xiaoxi Zhao<\/strong> is an Economist on the FBA Science team at Amazon. He earned his Ph.D. in Economics in 2017 from the University of Pittsburgh, specializing in applied microeconometrics at the intersection of environmental, urban, and health economics. After graduation, he joined Amazon Web Services (AWS), where he applied advanced econometric techniques to quantify and improve customers' cloud journeys and experiences. Two years later, he transitioned to his current role on the FBA Science team, where he leads a group of economists. In this position, he focuses on leveraging scalable causal machine learning techniques to generate insights that drive seller performance and business growth.<\/p> <p>\u00a0<strong>Ethan Dee<\/strong> is an Economist at Amazon. He holds a Ph.D. in Economics (2023) from the University of Illinois Urbana-Champaign, specializing in applied microeconometrics. His dissertation tackles the natural language processing task of classifying U.S. federal and state legislation into policy areas using a synthesis of supervised and unsupervised machine learning models, with an emphasis on model explainability and scalability. His dual expertise in causal inference and machine learning motivate his focus on causal machine learning methodologies. At Amazon, he applies large, high-dimensional datasets and cutting-edge identification strategies to measure seller performance and identify growth opportunities.<\/p> <p><strong>Vivian Yu<\/strong> is an Economist at Amazon, applying cutting-edge causal inference techniques to tackle complex challenges in third-party seller supply chain management. She finds immense fulfillment in seeing her work directly improve outcomes for the company's third-party selling partners. Prior to Amazon, she honed her analytical expertise in health care system, financial service, marketing and technology industries, developing a passion for using econometric methods to solve high-impact business problems and deliver tangible value to customers. She has a Bachelor\u2019s degree in Economics from Shanghai Jiao Tong University and a Ph.D. in Economics from State University of New York at Albany.<\/p><\/li><\/ul>\n<h3><strong><em><b><i>Digital Transformation in Transportation Systems: Navigating User Behavior and System Efficiency\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/i><\/b><\/em><\/strong><\/h3> <p>This tutorial explores the impact of digitalization on today\u2019s transportation systems, focusing on how emerging information technologies and pricing schemes are reshaping the travel behavior in congestible networks. Our focuses include: static and dynamic routing games, the impact of asymmetric information on network efficiency, and the design of incentives for system efficiency and equity. Through a combination of theoretical insights and empirical studies, the tutorial offers an in-depth analysis of models and tools for analyzing strategic user behavior, the role of information and incentive mechanisms in promoting socially desirable outcomes, and the application of these theories in real-world transportation systems.<\/p> <p><strong>Speakers:\u00a0 Haripriya Pulyassary and Manxi Wu<\/strong><\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong><b>Haripriya Pulyassary<\/b><\/strong> is a PhD student in the School of Operations Research and Information Engineering at Cornell University. Her research focuses on game theory and combinatorial algorithms with applications in transportation systems. Prior to joining Cornell, she obtained her Masters of Mathematics in Combinatorics &amp; Optimization (2022), and Bachelors in Computer Science (2021), from the University of Waterloo.<\/p> <p><strong><b>Manxi Wu<\/b><\/strong> is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University. Her research focuses on developing tools in game theory, information design, and market design, with applications in urban systems. Before joining Cornell, she was a Research Fellow at the Simons Institute for the Theory of Computing and a Postdoctoral Researcher in the EECS department at UC Berkeley (2022). Manxi earned her Ph.D. in 2021 from the Institute for Data, Systems, and Society at MIT. She also holds an M.S. in Transportation from MIT and a B.S. in Applied Mathematics from Peking University.<\/p><\/li><\/ul>\n<h3><strong><em><b><i>An Introduction to Decision Diagrams for Optimization\u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/i><\/b><\/em><\/strong><\/h3> <p>This tutorial provides an introduction to the use of decision diagrams for solving discrete optimization problems. A decision diagram is a graphical representation of the solution space, representing decisions sequentially as paths from a root node to a target node. By merging isomorphic subgraphs (or equivalent subproblems), decision diagrams can compactly represent an exponential solution space. This ability can reduce solving time and memory requirements potentially by orders of magnitude. That said, exact decision diagrams can still be of exponential size for a given problem, which limits their practical applicability to relatively small instances. However, recent research has introduced a scalable approach by compiling polynomial-sized relaxed and restricted diagrams that yield dual and primal bounds, respectively. These can be combined in an exact search to produce a generic decision diagram-based branchand-bound method. This chapter describes how this approach provides a scalable solution method for state-based dynamic programming models. In addition, the chapter shows how this approach can be applied to, and embedded in, other computational paradigms including constraint programming, integer programming, and column elimination. After this chapter, readers will have an understanding of the basic principles of decision diagram-based optimization, an appreciation of how it compares it to other optimization methods, and an understanding of what types of optimization problems are most suitable for this new technology.<\/p> <p><strong>Speaker:\u00a0<\/strong>Willem-Jan van Hoeve<\/p>\n<ul><li><h4>Speaker Bio<\/h4><p><strong>Willem-Jan van Hoeve<\/strong> is the Carnegie Bosch Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University. His research focuses on developing new methodologies for mathematical optimization with applications to network design, scheduling, vehicle routing, data mining, and others. He made notable contributions to the areas of constraint and integer programming, and most recently pioneered the field of decision diagrams for optimization. Van Hoeve\u2019s research has been funded by the National Science Foundation, the Office of Naval Research, and two Google Faculty Research Awards. He has consulted for a variety of companies including FedEx Ground, Exxon Mobil, PNC Bank, Bosch\/Siemens, and Charter Steel, as well as a number of non-profit organizations. Van Hoeve is the recipient of the INFORMS Computing Society Harvey J. Greenberg Research Award, the Tepper School's MBA Teaching Award (twice) and MSBA Teaching Award, and several best paper awards. He is currently Associate Editor for the INFORMS Journal on Computing and Editor of the journal Artificial Intelligence.<\/p><\/li><\/ul>","_links":{"self":[{"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/pages\/205","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/users\/46"}],"replies":[{"embeddable":true,"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/comments?post=205"}],"version-history":[{"count":149,"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/pages\/205\/revisions"}],"predecessor-version":[{"id":5967,"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/pages\/205\/revisions\/5967"}],"wp:attachment":[{"href":"https:\/\/meetings.informs.org\/wordpress\/seattle2024\/wp-json\/wp\/v2\/media?parent=205"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}