Scheduling elective surgeries: the tradeoff among bed capacity, waiting patients and operating room utilization using goal programming

Abstract

Scheduling of surgeries in the operating rooms under limited competing resources such as surgical and nursing staff, anesthesiologist, medical equipment, and recovery beds in surgical wards is a complicated process. A well-designed schedule should be concerned with the welfare of the entire system by allocating the available resources in an efficient and effective manner. In this paper, we develop an integer linear programming model in a manner useful for multiple goals for optimally scheduling elective surgeries based on the availability of surgeons and operating rooms over a time horizon. In particular, the model is concerned with the minimization of the following important goals: (1) the anticipated number of patients waiting for service; (2) the underutilization of operating room time; (3) the maximum expected number of patients in the recovery unit; and (4) the expected range (the difference between maximum and minimum expected number) of patients in the recovery unit. We develop two goal programming (GP) models: lexicographic GP model and weighted GP model. The lexicographic GP model schedules operating rooms when various preemptive priority levels are given to these four goals. A numerical study is conducted to illustrate the optimal master-surgery schedule obtained from the models. The numerical results demonstrate that when the available number of surgeons and operating rooms is known without error over the planning horizon, the proposed models can produce good schedules and priority levels and preference weights of four goals affect the resulting schedules. The results quantify the tradeoffs that must take place as the preemptive-weights of the four goals are changed.

A new model for operating room scheduling with elective patient strategy

“…The authors also proposed a two-level heuristic for solving this problem. Li et al (2017) developed two-goal programming MILP models to schedule elective operations based on the availability of surgeons and operating rooms considering goals of minimizing the number of waiting patients, minimizing the underutilization of the operating room, minimizing the maximum expected number of patients in the recovery unit; and minimizing the difference between the maximum and minimum expected number of patients in the recovery unit. Barrera et al (2020) developed a stochastic dynamic programming model with several weeks planning horizon that finds a near-optimal solution for the ORS with objectives of minimizing referral costs and due dates for all patients.…”

Operating Rooms Scheduling for Elective Surgeries in a Hospital Affected by War-Related Incidents

“…Li, Rafaliya, Baki and Chaouch [45] focused on four objectives i.e. decreasing the expected number of patients awaiting processing, maximising the utilisation of operating rooms, decreasing the prospective of patients in recovery rooms and estimating their average number.…”

A data-driven hierarchical MILP approach for scheduling clinical pathways: a real-world case study from a German university hospital

“…In most cases, mathematical programming, heuristics, column generation or combinations of these methods are used to find a good or even optimal solution with respect to a typically multi-criteria objective function. With regard to the scope of the present article, the main insights from the surgery scheduling literature can be summarized as follows: Overtime should be avoided and the well-being of the medical staff should be considered (Roland et al 2010;Meskens et al 2013), a flexible pooling strategy of OR has significant benefits (Batun et al 2011), the eligibility of Jebali et al (2006), Denton et al (2007), Lamiri et al (2007), Chaabane et al (2008), Lamiri et al (2008a, b), Cardoen et al (2009a), Fei et al (2009), Testi and Tànfani (2009), Denton et al (2010), Roland et al (2010), Batun et al (2011), Cardoen and Demeulemeester (2011), Marques et al (2012) and Clavel et al (2017) Beds Demeester et al (2010), Ceschia and Schaerf (2011), Schmidt et al (2013), Helm and Van Oyen (2014) and Vancroonenburg et al (2014) Treatments Vlah Jerić andFigueira (2010, 2012) and Schimmelpfeng et al (2012) Operating room and beds Pham and Klinkert (2008), Cardoen et al (2009b), Augusto et al (2010), Fei et al (2010), Chow et al (2011), Banditori et al (2013), Sun et al (2013), Vancroonenburg et al (2013), Ceschia and Schaerf (2014) and Li et al (2015) Clinical pathways…”

“…Ceschia and Schaerf (2014) address a similar patient admission problem considering OR utilization constraints, gender-separated room assignment, a flexible planning horizon and the notion of patient delay using a local search heuristic. Li et al (2015) develop a MILP-based lexicographic goal programming approach of scheduling OR and beds considering competing resources such as surgical and nursing staff, anesthesiologists and recovery beds.…”