An effective biogeography-based optimization algorithm for multi-objective green scheduling of distributed assembly permutation flowshop scheduling problem - Cheng, Long; Wang, Lei; Cai, Jingcao; Hu, Kongfu; Xiong, Yuan; Xia, Qiangqiang (Computers & operations research, 2025)
The distributed assembly permutation flowshop scheduling problem (DAPFSP) plays a crucial role in advancing distributed manufacturing systems (DMS). While much attention has been given to optimizing production scheduling for improved efficiency, energy consumption often remains overlooked. In line with sustainable development strategies, this research focuses on the multi-objective green scheduling of DAPFSP (MO-DAPFSP), introducing a mixed integer linear programming (MILP) model to minimize the maximum completion time and total machine energy consumption. To solve MO-DAPFSP, an effective biogeography-based optimization algorithm (EBBO) is proposed. EBBO incorporates a dual-population heuristic initialization method, specifically designed to generate high-quality initial solutions based on problem characteristics. A hybrid migration operator and an improved mutation operator are employed to enhance both global and local search capabilities. Additionally, a novel perturbation operator is integrated into the migration process, boosting the Pareto quality of partial solutions and accelerating convergence toward the true Pareto frontier. To evaluate the performance of EBBO, 810 instances of varying sizes were designed. The experimental results demonstrate that EBBO algorithm is highly effective in solving complex scheduling problems, providing a promising approach for optimizing multi-objective green scheduling in distributed manufacturing environments. •A model for MO-DAPFSP is established, aiming to minimize completion time and machine energy consumption.•A dual-population heuristic initialization method is designed to reduce redundant solutions and enhance initial solution quality.•A hybrid migration operator, combined with GA, is developed for global search in the algorithm.•An improved mutation operator is introduced as the local search method for the algorithm.•A permutation operator is designed to improve solution diversity and help escape local optima.