Thesis Defence: Minimax Regret Optimization with Applications in Healthcare Operations

Pooya Pourrezaiekhaligh, supervised by Dr. Babak Mohamadpour Tosarkani, will defend their thesis titled “Minimax Regret Optimization with Applications in Healthcare Operations” in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering.

An abstract for Pooya Pourrezaiekhaligh’s thesis is included below.

Defences are open to all members of the campus community as well as the general public. Registration is not required for in person defences.

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ABSTRACT

Uncertainty is an inherent characteristic of healthcare due to the complexity and variability of the healthcare system. The healthcare industry constantly evolves, and new medical technologies and treatments are being developed, leading to new challenges and opportunities. Managing uncertainty is critical for healthcare providers as it can impact the quality of care provided to patients and the financial performance of healthcare organizations. Therefore, developing effective strategies for managing uncertainty is essential for achieving better patient outcomes and optimizing resource allocation in healthcare.

This thesis explores the application of the regret minimization model to deal with demand uncertainty in a home health care location problem and uncertainty of surgery duration in an operating room planning problem. The objective is to find a balance between being conservative and being computationally efficient in decision-making under uncertainty. The literature review highlights the limitations of robust optimization models, which tend to be overly conservative and ignore the potential benefits of more optimistic scenarios. In contrast, regret minimization models provide a framework that accounts for potential regret in decision-making, leading to less conservative solutions.

In the home health care planning problem, the regret minimization model is applied to determine the optimal location of home health care facilities, considering demand uncertainty. The results show that the regret minimization model yields solutions that are less conservative than those obtained using the robust optimization model. In the operating room planning problem, the regret minimization model is applied to optimize the allocation of surgeries to operating rooms, considering the uncertainty of surgery duration. The results show that the regret minimization model outperforms the robust optimization model regarding conservatism.

Overall, this thesis demonstrates the effectiveness of the regret minimization model to deal with uncertainty in the home health care location and operating room planning problems.