Doctoral Students
Abhishrut's dissertation aims to develop an optimization-based decision support framework for assigning anesthesia providers to operating rooms, with the objective of reducing handovers while accounting for uncertainties in surgery durations. The framework will utilize advanced modeling approaches grounded in Stochastic Integer Programming (SIP). The study will focus on developing efficient, specialized algorithms to solve the resulting optimization models. Specifically, it aims to adapt techniques traditionally employed in Stochastic Linear Programming to the SIP framework, with a strong emphasis on polyhedral methods to improve computational efficiency. Data from a large tertiary medical center will be used to assess the performance of the proposed algorithms and derive managerial insights.
Mohammad's dissertation explores the application of the Branch and Price (B&P) framework to solve scheduling and resource allocation problems in healthcare delivery systems. The first application focuses on operational planning in surgery Pre-Admission Testing (PAT) clinics, where patients undergo necessary pre-surgery tests. Efficient operations in PAT clinics require effective coordination of patient appointment scheduling and optimal allocation of resources such as test rooms and nursing staff. The second application addresses Family Medicine Residency Scheduling (FMRS), which involves creating optimal annual rotation schedules for family medicine residents to ensure their training goals are met. The study aims to develop a B&P algorithm-based decision support framework tailored to these two applications. Central elements of the B&P algorithm, including the master problem, pricing subproblems, and branching strategies, will be specifically designed to enhance algorithm efficiency. Furthermore, the research will leverage the polyhedral structure of the master problem and pricing subproblems to strengthen their formulations. Customized heuristics will also be developed to generate high-quality solutions, aiding in the acceleration of the B&P algorithm. Data from large tertiary medical centers will be utilized to evaluate the performance of the proposed algorithms and derive managerial insights.
Mahdi's dissertation aims to create innovative decision support frameworks for allocating county budgets more effectively to combat the opioid epidemic, offering state policymakers new strategies to address this pressing issue. Current mitigation efforts often focus exclusively on either supply-side or demand-side interdiction strategies, overlooking the potential benefits of their combined, synergistic effects. This research adopts the perspective that mitigating the opioid epidemic requires a comprehensive approach, targeting illicit distribution networks from both the supply and demand perspectives. The proposed study seeks to integrate methodologies from Robust Optimization, Utility Theory, Machine Learning, and Network Interdiction to develop novel models that optimize budget allocations across both interdiction strategies. Focusing on West Virginia—the state with the highest opioid overdose death rate in the U.S.—the research will leverage data collected from various countywide and statewide sources. This data will be used to assess the outcomes of previous budget allocations on key performance indicators, such as opioid overdose deaths, drug-related violence, rehabilitation rates, and distribution of overdose reversal medications. The insights gained will inform the development and validation of data-driven models to generate actionable budget allocation strategies to address the opioid epidemic.