Distributed Dual-Resource Flexible Job Shop Scheduling Optimization Based on Multi-objective Gray Wolf Algorithm

With the development of global manufacturing, the distributed flexible job shop scheduling problem (DFJSP) has attracted much attention. However, DFJSPs that simultaneously consider constraints such as workers are rarely mentioned. Therefore, a distributed flexible job shop scheduling problem with dual resource constraints (DFJSP-DRC) is proposed in this paper, aiming to increase productivity while reducing energy consumption by scheduling machines and workers. A multi-objective optimization model with the goal of minimum energy consumption and makespan  is developed. In addition, an improved multi-objective gray wolf optimization algorithm (IMOGWO) is developed in this paper to solve the proposed problem. In IMOGWO, three scheduling rules and an active decoding strategy are designed to generate high-quality solutions in the initialization phase. One wolf predation strategy is designed to enhance the local search capability of the algorithm and expand the scope of solution space. Finally, the effectiveness of the IMOGWO in solving the DFJSP-DRC is verified through comprehensive experiments.