A multi-objective flexible flow shop scheduling problem with an improved NSGA-Ⅱ algorithm

作者：Xi Liu, Xi Liu, Xin Chen, Yushan Zhao, Xinbo Liu, Win-Chin Lin, Chin‐Chia Wu, Win-Chin Lin · 发表于：Journal of Industrial and Management Optimization · 年份：2025 · DOI：10.3934/jimo.2025042 · 被引用次数：6 · 研究领域：Scheduling and Optimization Algorithms、Advanced Manufacturing and Logistics Optimization、Advanced Control Systems Optimization

This paper addresses the multi-objective flexible flow shop scheduling problem, aiming to minimize makespan, reduce the ratio of processing delay to enhance customer satisfaction, and lower total electricity costs under a time-of-use pricing structure. Considering this problem's characteristics, the NSGA-Ⅱ is the algorithm chosen to best solve this problem. However, preliminary experiments reveal that NSGA-Ⅱ suffers from premature convergence, crossover between repeated solutions, and a time-consuming selection operator. To address these issues, a hybrid algorithm called HNSGA-Ⅱ, which combines Greedy Random Adaptive Search Procedure (GRASP) and NSGA-Ⅱ is designed, it introduces an adaptive crossover probability based on the similarity of parents to reduce the possibility of the crossover between close relatives, improves the population diversity, and incorporates an electoral selection based on the concept of Pareto to reduce the time cost. The test results demonstrate that the optimized HNSGA-Ⅱ outperforms the other methods in terms of the quality, quantity, and diversity of the Pareto solution set.