Multiple operation theatre scheduling for mitigating the disturbance caused by emergency patients

Abstract

Scheduling emergency patients is a problem that most hospitals struggle to solve without disturbing elective surgery patients' schedules. The present work undertakes this problem and considers scheduling elective patients surgeries in multiple operation theatres to mitigate the possible disturbance caused by emergency patient arrivals. The resultant problem has been termed as multiple operation theatre problems with total expected disturbance (MOTED). The number of elective surgeries and their corresponding surgery times are known and given in advance. However, emergency patient arrivals are stochastic in nature, which is tackled through scenario-generation techniques. The model assumes that emergency case scenarios can be predicted from historical data, and determines the sequence of elective patients in a multiple operation theatre such that the sum of the total expected disturbance (TED) caused by emergency patients and the total completion time of elective surgeries is minimized. The disturbance minimization increases the satisfaction level of patients, physicians and other medical staff, and indirectly reduces the overtime costs. The work provides an optimal algorithm for the MOTED problem with a single-operation theatre. Three heuristics and two metaheuristics have been proposed to solve the complete MOTED problem. The metaheuristic involves particle swarm optimization (PSO) and ant colony optimization (ACO). An extensive numerical experiment is performed using 48 randomly generated problem instances.