Feature-driven robust surgery scheduling
Yu Wang, Yu Zhang, Minglong Zhou, Jiafu Tang
Production and Operations Management · 2023 · Volume 32 · Issue 6
Abstract
Patient features such as gender, age, and underlying disease are crucial to improving the model fidelity of surgery duration. In this paper, we study a robust surgery scheduling problem augmented by patient feature segmentation. We focus on the surgery-to-operating room allocations for elective patients and future emergencies. Using feature data, we classify patients into different types using machine learning methods and characterize the uncertain surgery duration via a feature-based cluster-wise ambiguity set. We propose a feature-driven adaptive robust optimization model that minimizes an overtime riskiness index, which helps mitigate both the magnitude and probability of working overtime. The model can be reformulated as a second-order conic programming problem. From the reformulation, we find that minimizing the overtime riskiness index is equivalent to minimizing a Fano factor. This makes our robust optimization model easily interpretable to healthcare practitioners. To efficiently solve the problem, we develop a branch-and-cut algorithm and introduce symmetry-breaking constraints. Numerical experiments demonstrate that our model outperforms benchmark models in a variety of performance metrics.
Keywords
machine learning, overtime riskiness index, patient feature, robust optimization, surgery scheduling
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