Decision support algorithms for optimizing surgery start times considering the performance variation

Shing Chih Tsai (), Wu Hung Lin (), Chia Cheng Wu (), Shao Jen Weng () and Ching Fen Tang ()
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Shing Chih Tsai: National Cheng Kung University
Wu Hung Lin: National Cheng Kung University
Chia Cheng Wu: Cheng Ching General Hospital
Shao Jen Weng: Tunghai University
Ching Fen Tang: Cheng Ching General Hospital

Health Care Management Science, 2022, vol. 25, issue 2, No 3, 208-221

Abstract: Abstract In this paper, we consider a stochastic optimization model for a surgical scheduling problem with a single operating room. The goal is to determine the optimal start times of multiple elective surgeries within a single day. The term “optimal” is defined as the largest surgically related utility value while achieving a given threshold defined by the performance variation of a reference solution. The optimization problem is analytically intractable because it involves quantities such as expectation and variance formulations. This implies that traditional mathematical programming techniques cannot be directly applied. We propose a decision support algorithm, which is a fully sequential procedure using variance screening in the first phase, and then employing multiple attribute utility theory to select the best solution in the second phase. The numerical experiments show that the proposed algorithm can find a promising solution in a reasonable amount of time.

Keywords: OR in healthcare; Surgery scheduling under uncertainty; Surgery planned start time; Simulation-based optimization; Ranking and selection; Multiple attribute utility theory; Operations research (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10729-021-09580-2

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