Fuzzy Energy-Efficient Distributed Flow Shop Scheduling Based on Multi-Objective Biogeography Optimization Algorithm

In the face of increasingly severe ecological issues, sustainable development and green manufacturing have garnered significant attention. Meanwhile, with the development of globalization, distributed manufacturing is becoming more prevalent. In this paper, the Fuzzy Energy Efficient Distributed Permutation Flow Shop Problem (FEEDPFSP) is studied, with the objective to minimize fuzzy makespan and fuzzy energy consumption. A Multiple-Objective Biogeography-Based Optimization (MOBBO) is proposed for FEEDPFSP. In MOBBO, an effective initial solution generation rule is designed, and according to the relationship between the two optimization objectives, four speed operation methods are designed for the migration process and the mutation process, and fast non-dominated sorting and crowding distance methods are introduced to ensure the population quality of each iteration. A comparison of the performance of the proposed MOBBO algorithm with two state-of-the-art algorithms across 480 instances of different scales demonstrates the effectiveness of the algorithm in addressing the FEEDPFSP.