关键词: 分布式柔性作业车间调度问题, 多工厂协同生产, 改进差分进化算法, 双目标优化模型, 总成本, 提前/延期惩罚

Abstract: The single-factory manufacturing is gradually transiting to the multi-factory collaborative production with the economic globalization,and the distributed resource with diverse demand make it more challenged to schedule the production among multiple factories effectively.For this reason,the Distributed Flexible Job Shop Scheduling Problem (DFJSP) was studied.A bi-objective optimization model was formulated to minimize the earliness/tardiness and the total cost simultaneously.An Improved Differential Evolution Simulated Annealing Algorithm (IDESAA) was proposed,and two crossover and mutation operators were designed.With the strong robustness,simulated annealing was used to local search the best Pareto solutions.The greedy selection combined with the fast elitist Non-Dominated Sorted Genetic Algorithm's (NSGA-Ⅱ) selection was employed to select the offspring.The comprehensive experiments were conducted and the results showed that the proposed algorithm could solve DFJSP effectively and efficiently.

Key words: distributed flexible job shop scheduling problem, multi-factory collaborative production, improved differential evolution algorithm, bi-objective optimization model, cost, earliness/tardiness