Surgical case scheduling problem with fuzzy surgery time: An advanced bi-objective ant system approach

Abstract

We address the bi-objective surgical case scheduling problem under uncertain service times. The goal is to simultaneously minimize (i) makespan and (ii) number of unscheduled surgical cases. We optimize two decisions in our surgical case scheduling problem: the allocation of the resources to the surgical cases and their starting times. We formulate our problem as a novel bi-objective no-wait multi-resource flexible job shop problem. We use fuzzy numbers to represent the inherent stochasticity in the length-of-stays of patients in different stages of an operating theater. Due to the intractability of the problem even for small instances, we develop a novel bi-objective ant system: Fuzzy Pareto Envelope-based Selection Ant System. The performance of the new algorithm on all test instances is compared to a basic bi-objective ant system under the fuzzy condition: Pareto strength ant colony optimization. Finally, we demonstrate computationally that our approach outperforms the state-of-the-art algorithm in literature in terms of both efficiency and effectiveness.