Hybridizing Whale Optimization Algorithm With Particle Swarm Optimization for Scheduling a Dual-Command Storage/Retrieval Machine

Abstract

Whale optimization algorithm (WOA) and particle swarm optimization (PSO) have been used individually usually. However, a separate use of them has a limitation. Hybridizing WOA with PSO is expected to evolve solutions better due to the cooperation between whales and seabirds. Developing such kind of model is the focus of this research. A framework has been further proposed to best utilize such hybridizations for developing simulation-based optimization approaches. The framework has the advantage of integrating metaheuristic, simulation, and optimization seamlessly. It can waive the rigorous and labor-intensive optimization procedure required for traditional simulation. In this research, simulation-based optimization approaches are used to deal with the dual-command block scheduling problem of a manufacturing firm’s storage/retrieval (S/R) machine in an automated storage/retrieval system. The S/R machine is mainly used to store/retrieve stock-keeping units in an automated storage/retrieval system. Three simulation-based optimization approaches, Hybrid1 (WOA+PSO), Hybrid2 (WOA+PSO), and Hybrid3 (WOA+PSO), have been developed. To investigate their effectiveness, experiments have been conducted to compare them with their base models, WOA and PSO, as well as the genetic algorithm (GA) and PWOA. The PWOA is an abbreviation of a hybridization of PSO and WOA proposed in a previous study. The experimental results show that Hybrid3 (WOA+PSO) outperforms Hybrid2 (WOA+PSO), Hybrid1 (WOA+PSO), WOA, PSO, PWOA, and GA. The uses of techniques such as hybridization, Neighborhood heuristic, and adaptive movements of whales empower Hybrid3 (WOA+PSO) the most.