Selected ABW research projects

Optimizing hospital patient flow and resource allocation
Dimitris Bertsimas and Liangyuan Na
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In this project, we first develop and implement predictive machine learning models to predict patient flows at a major hospital (Bertsimas, Pauphilet, Stevens and Tandon). Then, we integrate these predictions into an overall bed recommendation engine, that unifies the bed assignment process across the entire hospital, and accounts for request and availability of beds in all units, currently and through the rest of the day.
Dimitris Bertsimas and MIT
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The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.
Advanced analytics for better understanding the spread of Alzheimer’s disease
Reza Mohammadi and Martin Dyrba
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Alzheimer’s disease (AD) is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. Markov random fields are a probabilistic graphical modeling approach that represent the interaction between individual random variables by an undirected graph. We propose the novel application of this approach to study the interregional associations and dependencies between multimodal imaging markers of AD pathology and to compare different hypotheses regarding the spread of the disease.
Optimizing Influenza Vaccine Composition
Hari Bandi and Dimitris Bertsimas
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In this project, we propose a holistic framework based on state-of-the-art methods in machine learning and optimization to prescribe influenza vaccine composition that are specific to a region, or a country. Through numerical experiments, we show that our proposed vaccine compositions could potentially lower morbidity by 8-10% and mortality by 6-9% over vaccine compositions proposed by the U.S. Food and Drug Administration.
Urban Object Detection Kit: A System for Collection and Analysis of Street-Level Imagery
Maarten Sukel and Stevan Rudinac
City of Amsterdam
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In this project, we propose Urban Object Detection Kit, a system for the real-time collection and analysis of street-level imagery. The system is affordable and portable and allows local government agencies to receive actionable intelligence about the objects on the streets.
The Nutritious Supply Chain: Optimizing Humanitarian Food Aid
Koen Peters and Hein Fleuren
Zero Hunger Lab
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To deal with the operational complexities inherent in its mandate, WFP has been developing tools to assist its decision makers with integrating supply chain decisions across departments and functional areas. This paper describes a mixed integer linear programming model that simultaneously optimizes the food basket to be delivered, the sourcing plan, the routing plan, and the transfer modality of a long-term recovery operation for each month in a predefined time horizon
Bus Routing Optimization Helps Boston Public Schools Design Better Policies
Dimitris Bertsimas and Arthur Delarue
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We propose an algorithm to jointly solve the school bus routing and bell time selection problems. Our application in Boston led to $5 million in yearly savings (maintaining service quality despite a 50-bus fleet reduction) and to the unanimous approval of the first school start time reform in 30 years.
Identifying child abuse through text mining and machine learning
Dr. Chintan Amrit and University of Amsterdam Business School
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We use text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse.
Quick Scan Tool for water allocation in the Netherlands
Peter Gijsbers and Jorn Baayen
University of Amsterdam Business School
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This Quick Scan Tool uses a coarse scale network model of the Netherlands water system to compute the water allocation pattern given water demands and boundary conditions as provided by the National Hydrological Model. To accommodate the priority based water allocation policies commonly used in the Netherlands, a lexicographic goal programming technique is used to solve the water allocation problem.
Economically Efficient Standards to Protect the Netherlands Against Flooding
Dick den Hertog and Carel Eijgenraam
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 In the Netherlands, flood protection is a matter of national survival. In 2008, the Second Delta Committee recommended increasing legal flood protection standards at least tenfold to compensate for population and economic growth since 1953; this recommendation would have required dike improvement investments estimated at 11.5 billion euro. Our research group was charged with developing efficient flood protection standards in a more objective way. Compared to the earlier recommendation, this successful application of operations research yields both a highly significant increase in protection for these regions (in which two-thirds of the benefits of the proposed improvements accrue) and approximately 7.8 billion euro in cost savings.
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