My research interests lie in decomposition methods for large-scale optimization, supply chain management analytics, transportation and logistics, healthcare analytics, and problems that arise at the interfaces of operations research and machine learning.
In one stream, I look for specific structures in the problems that can be exploited for development of mathematical models and solution algorithms to deliver strategic, tactical or operational insights. I apply both deterministic and stochastic modeling and solution techniques. My studies in this stream (1) consider revenue management decisions in profit-maximizing hub location problems, (2) devise efficient algorithms for Nash-bargaining-based models for matching markets in industrial grade applications, (3) develop mathematical models and solution methods for staffing and scheduling physicians in hospitals in the face of uncertainty in the availability of doctors, and (4) devise a data-driven framework for concluding a suspended sports league after hiatus due to, e.g., a pandemic or player strikes.
The second stream involves theory of mathematical/integer programming, where I look for general-purpose techniques for accelerating decomposition methods for large-scale optimization. Such challenging problems lie at the heart of operations research with a wide spectrum of applications ranging from supply chain management and scheduling, to managerial applications such as decision making under uncertainty, and problems arising in machine learning and computational biology. In particular, (1) one of my papers accelerates Benders Decomposition (BD) by introducing deepest Benders cuts and efficient algorithms for deriving these cuts. On the application side, (2) two of my papers employ BD integrated with combinatorial algorithms for solving large instances of variants of Hub Location problems under different types of uncertainties, and (3) another study introduces a Branch and Cut algorithm based on BD for the Minimum Graph Bisection problem. For an up-to-date list of publications, please see my Google Scholar page.
Publications:
- “Robust-Stochastic Models for Profit Maximizing Hub Location Problems” (2021), Transportation Science, (with Gita Taherkhani and Sibel A. Alumur)
- “Benders Decomposition for Profit Maximizing Hub Location Problems with Multiple Demand Classes” (2020), Transportation Science, (with Gita Taherkhani and Sibel A. Alumur)
- “Nash-Bargaining-Based Models for Matching Markets, with Implementations and Experimental Results”, The 13th Innovations in Theoretical Computer Science Conference (ITCS 2022), (with Vijay V. Vazirani)
Working Papers:
- “Deepest Cuts for Benders Decomposition”, major revision, Operations Research, arXiv preprint, (with John G. Turner)
- “How to Conclude a Suspended Sports League?”, revising for re-submission, Manufacturing & Service Operations Management, (with Ali Hassanzadeh and John G. Turner)
Research in Progress:
- “Staffing, Scheduling and Rescheduling of Physicians in Hospitals in the Face of Uncertainties”, (with Christiane Barz and John G. Turner)
- “Accelerating Benders Decomposition via Predictive Cuts”, (with John G. Turner)
- “Branch and Benders Cut for the Minimum Graph Bisection Problem”, (with Ali Hassanzadeh)