Scheduling operating rooms with consideration of all resources, post anesthesia beds and emergency surgeries. (July 2016)

• Record Type: Journal Article
• Authors: Latorre-Núñez, Guillermo; Lüer-Villagra, Armin; Marianov, Vladimir; Obreque, Carlos; Ramis, Francisco; Neriz, Liliana
• Is Part Of: Computers & industrial engineering. Volume 97(2016)
• Page Start: 248
• Page End: 257
• Publication Date: 2016-07
• Subjects: Operating room scheduling -- Emergency surgeries -- Hybrid flow shop -- Constraint programming -- Genetic algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.05.016
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We propose two exact and a metaheuristic method for surgery room scheduling. We take into consideration constrained human and physical resources. The scheduling considers the availability of post-anesthesia recovery beds. We ensure adequate inter-surgery intervals for attending emergency surgeries. Abstract: Surgery rooms are among the most expensive resources in hospitals and clinics. Their scheduling is difficult because, in addition to the surgical room itself, each surgery requires a particular combination of human resources, as well as different pieces of equipment and materials. Furthermore, after each surgery, a post-anesthesia bed is required for the patient to recover. Finally, in addition to planned surgeries, the scheduling must be made in such a way as to accommodate the emergency surgeries that may arrive during each day, which must be attended within a limited time. We address the surgery scheduling problem considering simultaneously, for the first time, the operating rooms, the post anesthesia recovery, the resources required by the surgery and the possible arrival of emergency surgeries. We propose an integer linear programming model that allows finding optimal solutions for small size instances, we transform it to use constraint programming, and develop a metaheuristic based on a genetic algorithm and a constructive heuristic, that solves larger size instances. Finally, we present numerical experiments.