A discrete squirrel search algorithm for the surgical cases assignment problem

Abstract

Surgical cases assignment problem (SCAP) is among the most investigated parts in healthcare scheduling and assignment problems, in which a set of surgical cases are assigned to operating rooms within a specified planning horizon. Several methods have been developed to provide approximate solutions for SCAP. Nevertheless, existing methods underperform at the large-scale instances. In this paper, a discrete squirrel search algorithm (DSSA) is proposed for SCAP with the objective of minimizing total operating cost. First, four heuristics are presented to improve quality and diversity of initial population. Second, a surgical case sequence vector is employed to encode individuals, and a corresponding decoding scheme is designed to construct feasible schedules. Third, several efficient heuristics are embedded into DSSA to enhance the search capacity. Moreover, the Taguchi method of design-of-experiment (DOE) is adopted to explore the influence of parameter settings. To the best of our knowledge, it is the first application of the squirrel search algorithm for SCAP. The effectiveness of DSSA is conducted on a typical benchmark dataset. Computational results and comparisons demonstrate the superiority of the proposed scheme over the existing methods in solution accuracy and consuming time for solving SCAP.