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

Intelligent machines and automated guide vehicles (AGVs) have been widely applied to improve the flexibility of manufacturing systems. However, the energy consumption and battery management of AGVs are not well considered. Therefore, this paper proposes an integrated scheduling method for solving flexible job shop scheduling problems (FJSP) with energy-efficient AGVs to minimize the makespan and energy consumption. Besides, a novel hybrid genetic algorithm (HGA) with a local neighbor search (LNS) is developed to optimize the objective. Results indicate that the proposed HGA obtains a shorter makespan and lower energy consumption than the genetic algorithm. Finally, we verify the model and present the integrated scheduling solutions of FJSP with energy-efficient AGVs. It indicates the proposed method has significant potential for intelligent manufacturing systems.