Title: Stochastic goal programming and metaheuristics for the master surgical scheduling problem

Authors: Justin Britt; Xiangyong Li; Ahmed Azab; Mohammed Fazle Baki

Addresses: Production and Operations Management Research Lab, University of Windsor, Windsor, Canada ' School of Economics and Management, Tongji University, Shanghai, China ' Production and Operations Management Research Lab, University of Windsor, Windsor, Canada ' Odette School of Business, University of Windsor, Windsor, Canada

Abstract: Planning and scheduling in a hospital require the consideration of several competing objectives, stakeholders, and resources. In this paper, methods for the master surgical scheduling problem (MSSP), which involves assigning surgeons to time blocks in operating rooms (ORs), are proposed. A stochastic weighted goal programming model (WGPM) with four goals and metaheuristics are used to perform elective surgery scheduling under uncertainty of both surgical durations and patient lengths of stay. In addition, discrete event simulation (DES) models and a decision support system (DSS) are developed. Computational experiments are used to evaluate the WGPM, validate the DES models, assess the relationships between the goals, and to tune and evaluate the metaheuristics. Results show that even though there are trade-offs between the goals that must be considered, it is possible to attain a high level of OR utilisation while meeting strategic targets and optimising recovery ward (RW) utilisation.

Keywords: operating room planning and scheduling; tactical planning; master surgical scheduling; decision support system; DSS; stochastic goal programming; discrete event simulation; DES.

DOI: 10.1504/IJOR.2022.121492

International Journal of Operational Research, 2022 Vol.43 No.1/2, pp.5 - 41

Received: 21 Sep 2019
Accepted: 19 Apr 2020
Published online: 16 Mar 2022 *

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