Appointment scheduling of outpatient surgical services in a multistage operating room department

This article addresses the appointment scheduling of outpatient surgeries in a multistage operating room (OR) department with stochastic service times serving multiple patient types. We discuss many challenges, such as the limited availability of multiple resources (e.g., staff, operating rooms, surgeons, and recovery beds), and the compatibility of patient and surgeon types. In addition, availability of surgeons is restricted by time window constraints. Three simulation-based optimization methods have been proposed to minimize the patients' wait time, patients' completion time, and number of surgery cancellations. The first method is simulation-based tabu search (STS). It combines discrete-event simulation and tabu search to schedule surgery cases. The second and third methods are integer programming enhanced tabu search (IPETS) and binary programming enhanced tabu search (BPETS). IPETS and BPETS improve on STS by incorporating integer programming and binary programming models, respectively. This article includes a case study of an OR department in a major Canadian hospital. We further expand the actual data obtained in the case study to cover a wide range of parameters in sets of test problems, and provide analysis on the efficiency and effectiveness of the proposed methods in comparison with several scheduling rules. Finally, comments on the applications of the proposed methods are provided.