Hybridization of Whale Optimization Algorithm for Multi-objective Flow Shop Scheduling Problems

Flow Shop Scheduling Problem (FSSP) is a class of scheduling problems in the manufacturing industry. The goal of the FSSP is to find a schedule, namely the order in which jobs are executed in such a way that they meet specific optimization criteria. FSSP with more than one optimization criterion is called Multi-objective FSSP (MOFSSP). Due to the high complexity, non-exact approaches are commonly used to solve this problem. A widely used technique is metaheuristics. This paper investigates the Whale Optimization Algorithm (WOA) metaheuristic, combined with the well-known metaheuristic Genetic Algorithm (GA) through hybridization, to solve MOFSSP problems with the objective criteria of makespan and total flow time. The hybridization is done by combining the essential components of WOA and the genetic operations of GA. $hg-WOA$, the proposed algorithm, has four versions: $hg-WOA-V1$, $hg-WOA-V2$, $hg-WOA-V3$, and $hg-WOA-V4$. The performance of each version is measured using the Taillard Benchmark. Based on the experiments conducted, the proposed four algorithms are promising. All algorithms perform better in solving single-objective FSSP and three algorithms outperformed the original WOA in solving multi-objective FSSP.