Integrated Scheduling of Machines and Automated Guided Vehicles in Energy-Efficient Flexible Job Shop

With growing concerns over environmental and energy issues, the manufacturing industry must enhance energy efficiency. However, existing research on flexible job shop scheduling (FJSP) often overlooks transportation and comprehensive energy models. This paper addresses this gap by proposing an integrated scheduling model of machines and automated guided vehicles in Energy-Efficient Flexible Job Shop (EFJSP-AGVs). To solve it, an improved whale optimization algorithm with genetic operators (IWOA-GA) is developed. First, a three-segment encoding and hybrid initialization strategy are used to generate solutions for EFJSP-AGVs’ sub-problems. Then, genetic operators are introduced to discretize WOA, and a critical path-based variable neighborhood search enhances search efficiency. Comparative experiments on 14 benchmark datasets with two advanced algorithms show that IWOA-GA performs significantly better in solving EFJSP-AGVs.