2024年9月1日，Elsevier 旗下top期刊《Engineering Applications of Artificial Intelligence》在线发表了云南师范大学数学学院李俊青教授团队的最新研究成果《Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm》。云南师范大学数学学院李俊青教授为通讯作者。合作单位为山东师范大学、恒星学院（HengXing University）
https://www.sciencedirect.com/science/article/abs/pii/S0952197624013782
Abstract
In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.
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