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Efficient optimization algorithms for surgical scheduling under uncertainty

delete2021-09-01
delete15
PRE
AI
S
Shing Chih Tsai *
Y
Yingchieh Yeh
C
Chen Yun Kuo
DOI:10.1016/j.ejor.2020.12.048delete
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Abstract

Abstract

En 中文
In this paper, we develop a stochastic optimization model for a surgical scheduling problem considering a single operating room. We arrange a set of elective surgeries into appropriate time blocks, and determine their planned start time and specific sequence. Due to the complexity of the original formulation, we reformulate our model as a two-stage mixed-integer problem. We consider the planning decision in the first stage and the sequencing decision in the second stage (based on the first one). The goal of this paper is to obtain a nearly optimal schedule in reasonable computational time. The term optimal is defined as the lowest surgically related cost while achieving the given threshold with respect to some specific deterministic or stochastic performance measures. The optimization model involves expected and probabilistic formulations that are analytically intractable. This implies that traditional mathematical programming techniques cannot be used directly. Therefore, we propose adapted rapid-screening and stochastic-approximation algorithms to deal with the first-stage and the second-stage problems, respectively. In both algorithms, we can apply either the Laplace transform or simulation methods to either evaluate or estimate the desired performance measures. The experimental results demonstrate that the proposed algorithms are more favorable compared to existing approaches. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
OR in healthcare
Surgery scheduling under uncertainty
Surgery planned start time
Simulation optimization
Laplace transform

Journal

European Journal of Operational Research cover
European Journal of Operational Research
IF:
6
Papers:
2.2W
Citations:
6.4W

Organization

N
National Cheng Kung University
Scholars:
2.6W
Papers: 2.3W
Citations: 1.7W
N
National Central University
Scholars:
1.0W
Papers: 8.6K
Citations: 6.4K
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