INFORMS 2021 Program Book

INFORMS Anaheim 2021

SB20

3 - Data-Driven Certification of Neural Networks with Random Input Noise Brendon Anderson, University of California-Berkeley, Berkeley, CA, 94709-1543, United States A novel robustness certification method is introduced that lower-bounds the probability that neural network outputs are safe when the input is subject to random noise from an arbitrary probability distribution. The bound is cast as a chance-constrained optimization problem, which is then reformulated using input-output samples to make the optimization constraints tractable. We develop sufficient conditions on the number of samples needed to make the robustness bound hold with overwhelming probability, and we show for a special case that the proposed optimization reduces to an intuitive closed-form solution. Synthetic, MNIST, and CIFAR-10 case studies experimentally demonstrate that this method is able to certify robustness against various input noise regimes over larger uncertainty regions than prior state-of-the-art techniques.

2 - From Data to Prescriptions: An Optimization Framework for Treatment Personalization Holly Mika Wiberg, Massachusetts Institute of Technology, Cambridge, MA, 02144-2603, United States, Dimitris Bertsimas Personalized treatment involves several complex decisions, particularly in the presence of multiple treatment options and continuous dosages. We propose a joint machine learning and optimization framework for treatment prescriptions, in which we leverage ML to learn treatment effects from data and formulate a mixed-integer programming model to identify promising regimens from the ML models. The approach generalizes to multiple treatment objectives and risk tolerances, as well as additional clinically-derived constraints. We demonstrate the method in chemotherapy as well as chronic disease management. 3 - Prioritizing Substance Abuse Treatment in Community Corrections Centers. Iman Attari, Indiana University, Bloomington, IN, United States Pengyi Shi, Jonathan Eugene Helm, Nicole Adams With overcrowding becoming more common in correctional centers due to the increasing trend in substance abuse, it is becoming increasingly important to take measures to prevent relapse and recidivism for community corrections clients. Although different treatment options have been found to be effective, particularly for clients suffering from substance use disorder, correctional organizations have a limited budget to deploy these interventions. In this study, we propose a modeling framework to support substance abuse treatment prioritization decisions in community corrections centers. Specifically, we propose a Markov Decision Process modeling framework for identifying the timing and target of treatment interventions among community corrections clients, capturing the resulting impact on overcrowding and societal benefits from client recovery. SB20 CC Room 203B In Person: Capacity Management in Healthcare and Care Coordination in Health Systems General Session Chair: Christos Zacharias, University of Miami, Coral Gables, FL, 33146-2000, United States Chair: Salar Ghamat, Wilfrid Laurier University, Waterloo, N2L 3C5, Canada 1 - Dynamic Inter-day and Intra-day Scheduling Christos Zacharias, University of Miami, Coral Gables, FL, 33146- 2000, United States, Nan Liu, Mehmet A. Begen We present novel theoretical results and the first tractable optimization framework for the dynamic inter-day and intra-day scheduling problem. In our analysis we built upon the findings of Truong (2015) and Zacharias and Yunes (2020), we prove theoretical connections between them, and we prove novel results in discrete convex analysis regarding constrained multimodular function minimization. We leverage these novel results and dynamic programming tools to characterize an optimal policy. We derive theoretical upper and lower bounds for the problem, based on which we develop a heuristic solution with a theoretically guaranteed optimality gap. The gap is demonstrated numerically to be less than 1% for practical instances of the problem. 2 - Influencing Primary Care Antibiotic Prescription Behavior Using Financial Incentives Salar Ghamat, Wilfrid Laurier University, Waterloo, ON, Canada, Mojtaba Araghi, Lauren Cipriano Antibiotic resistance is an ongoing public health crisis that is escalated by overuse and misuse of antibiotics. The goal of this paper is to examine the impact of incentive payments on reducing inappropriate antibiotic prescription. We develop a stylized physician compensation model to study the interaction between a payer that aims to reduce social harm from antibiotic resistance, and a provider who makes antibiotic prescription decisions for heterogeneous patients. We show that when there is no information asymmetry between the parties, an incentive payment can achieve the first-best policy even when incentive payments affect diagnosis behaviour of the provider. However, when the payer does not know the costs incurred by the provider the first-best policy is not possible when incentive payments affect provider’s diagnosis behaviour.

SB18 CC Room 202B In Person: Energy Systems Integration (Macro-Energy Systems) General Session

Chair: Wilson Ricks, Princeton University, Princeton, NJ, United States 1 - Modeling Potential Roles for Nuclear Power in Microgrid Settings with Integrated Heat and Power Systems Ruaridh Macdonald, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States, John E. Parsons Small nuclear reactors, with 10MWe output or less, have been proposed for deployment in remote communities. Their reliability and ability to provide combined heat and power at high temperatures could potentially reduce energy costs and emissions. However, there is uncertainty about the range of circumstances for which this is true. In this work, we extended the GenX capacity expansion model to be able to optimize integrated heat and electricity systems. We then used this to investigate the impact of introducing small nuclear reactors to several representative Alaskan communities with a variety of heat and electricity demand profiles and degrees of integration between the two. 2 - The Impact of Flexible Operations and Energy Storage on the Long-term Deployment Potential of Enhanced Geothermal Systems Wilson Ricks, United States Enhanced Geothermal Systems (EGS) are an emerging energy technology with the potential to provide clean, firm electricity generation across much of the western United States. While EGS has traditionally been envisioned as providing baseload power, these systems are in fact capable of operating flexibly by storing energy as pressure within the engineered subsurface reservoir. Past work has shown that this flexibility can deliver significant additional value. In the present work, we develop novel approach by which constraints describing the unique flexible geothermal technology can be incorporated into the GenX electricity systems optimization model. Analysis indicates that flexible operations can significantly increase the deployment of EGS power in the Western Interconnection and reduce total system costs. SB19 CC Room 203A In Person: Interface between Healthcare and Criminal Justice/Learning in Healthcare General Session Chair: Pengyi Shi, Purdue University, West Lafayette, 47907, United States 1 - Causal Inference with Selectively Deconfounded Data Kyra Gan, Carnegie Mellon University, Forbes Avenue Tepper School Of Business Center Dr, Pittsburgh, PA, 15205, United States, Andrew Li, Zachary Lipton, Sridhar R. Tayur We consider the benefit of incorporating a large confounded observational dataset (confounder unobserved) alongside a small deconfounded observational dataset (confounder revealed) when estimating the Average Treatment Effect (ATE). We show that the inclusion of confounded data can significantly reduce the quantity of deconfounded data required to estimate the ATE to within a desired accuracy level. Moreover, when we could retrospectively select samples to deconfound, we demonstrate that by actively selecting these samples based upon the (already observed) treatment and outcome, we can reduce our data dependence further. Our theoretical results establish that the worst-case relative performance of our approach (vs. a natural benchmark) is bounded while our best-case gains are unbounded. We perform extensive experiments to validate our theoretical results.

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