Saumya Sinha

Approximate solution of infinite-horizon risk-sensitive Markov decision processes

Categories Dynamic Programming, Other Topics Tags , ,

Infinite-horizon risk-sensitive Markov decision processes (MDPs) under the discounted cost criterion are challenging to solve because the optimal policy may be nonstationary. Existing methods  typically reformulate the problem as a continuous-state risk-neutral MDP and rely on state discretization or value-function approximation, often without explicit stopping conditions or error bounds. In this paper, we present approximate … Read more

Markov Decision Process Design: A Framework for Integrating Strategic and Operational Decisions

Categories Integer Programming, Nonlinear Optimization Tags , ,

We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem … Read more

Characterizing Rational Transplant Program Response to Outcome-Based Regulation

Categories Applications - OR and Management Sciences Tags , , ,

Organ transplantation is an increasingly common therapy for many types of end-stage organ failure, including lungs, hearts, kidneys and livers. The past twenty years have seen increased scrutiny of post-transplant outcomes in the United States, in order to ensure the efficient utilization of the scarce organ supply. Under regulations by the Organ Procurement Transplantation Network … Read more

Relaxations and Duality for Multiobjective Integer Programming

Categories Integer Programming, Multi-Criteria Optimization Tags , , , ,

Multiobjective integer programs (MOIPs) simultaneously optimize multiple objective func- tions over a set of linear constraints and integer variables. In this paper, we present continuous, convex hull and Lagrangian relaxations for MOIPs and examine the relationship among them. The convex hull relaxation is tight at supported solutions, i.e., those that can be derived via a … Read more