Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem

• 成果类型: Article
• 署名作者: Tsang, Man Yiu; Shehadeh, Karmel S.; Curtis, Frank E.; Hochman, Beth R.; Brentjens, Tricia E.
• 署名单位: Lehigh University; NewYork-Presbyterian Hospital; Columbia University; Columbia University; NewYork-Presbyterian Hospital
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0258
• 发表日期: 2025
• 关键词: distributionally robust optimization; uncertain surgery durations; elective surgeries; health-care; DECOMPOSITION; methodology; EFFICIENCY; CHALLENGES; MODEL

摘要

We propose combined allocation, assignment, sequencing, and scheduling problems under uncertainty involving multiple operation rooms (ORs), anesthesiologists, and surgeries as well as methodologies for solving such problems. Specifically, given sets of ORs, regular anesthesiologists, on -call anesthesiologists, and surgeries, our methodologies solve the following decision -making problems simultaneously: (1) an allocation problem that decides which ORs to open and which on -call anesthesiologists to call in, (2) an assignment problem that assigns an OR and an anesthesiologist to each surgery, and (3) a sequencing and scheduling problem that determines the order of surgeries and their scheduled start times in each OR. To address the uncertainty of each surgery's duration, we propose and analyze stochastic programming (SP) and distributionally robust optimization (DRO) models with both risk -neutral and risk -averse objectives. We obtain near -optimal solutions of our SP models using sample average approximation and propose a computationally efficient column -andconstraint generation method to solve our DRO models. In addition, we derive symmetrybreaking constraints that improve the models' solvability. Using real -world, publicly available surgery data and a case study from a health system in New York, we conduct extensive computational experiments comparing the proposed methodologies empirically and theoretically, demonstrating where significant performance improvements can be gained. Additionally, we derive several managerial insights relevant to practice.