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Chance-Constrained Surgery Planning Under Conditions of Limited and Ambiguous Data

Yan Deng (), Siqian Shen () and Brian Denton ()
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Yan Deng: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Siqian Shen: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Brian Denton: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109

INFORMS Journal on Computing, 2019, vol. 31, issue 3, 559–575

Abstract: Surgery planning decisions include which operating rooms (ORs) to open, allocation of surgeries to ORs, sequence, and time to start each surgery. They are often made under uncertain surgery durations with limited data that lead to unknown distributional information. Moreover, cost parameters for criteria such as overtime and surgery delays are often difficult or impossible to estimate in practice. In this paper, we formulate distributionally robust (DR) chance constraints on surgery waiting and OR overtime, which recognize practical limitations on data availability and cost parameter accuracy. We use ϕ -divergence measures to build an ambiguity set of possible distributions of random surgery durations, and derive a branch-and-cut algorithm for optimizing a mixed-integer linear programming reformulation based on finite samples of the random surgery durations. We test instances generated from real hospital-based surgery data. The results show computational efficacy of our approaches, and provide insights for DR surgery planning.

Keywords: allocation and scheduling; chance-constrained programming; distributionally robust optimization; φ -divergence; mixed-integer linear programming; branch-and-cut (search for similar items in EconPapers)
Date: 2019
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