多目标模糊柔性作业车间调度问题优化

Optimization of Multi-objective Fuzzy Flexible Job Shop Scheduling Problem

为降低柔性作业车间调度中的能耗,针对实际制造车间中工序加工时间和交货期的不确定性,将加工时间和交货期采用模糊数表示,建立以完工时间、平均满意度和最小满意度为柔性作业车间调度问题的多目标函数。同时设计了邻域遗传算法(GANS)求解该问题,算法采用机器选择的方法产生初始种群,并采用工序插入式方法对染色体进行解码;采用动态交叉概率及改进精英保留策略来保证种群的多样性和加快算法的收敛速度;并提出一种基于移动模糊关键工序的邻域结构来加强算法的局部搜索能力。最后通过数值实验验证了模型和算法的有效性和可行性,并对4个基准问题进行测试。结果表明:该算法在求解的精度、鲁棒性和解集的分布性方面与传统算法相比具有一定的优势,是一种有效的求解模糊柔性作业车间调度问题的新方法。

In order to reduce the energy consumption in the flexible job shop scheduling,the processing time is determined by the fuzzy number for the uncertainty of the processing time in the actual manufacturing workshop,makespan,average satisfaction and minimum satisfaction are supposed as multi-objective function for flexible job shop scheduling problem(FJSP).A neighborhood search genetic algorithm(GANS)was designed to solve the problem.The algorithm uses the machine selection method to generate the initial population,and uses the process plug in method to decode the chromosomes.The dynamic crossover probability and the elitism retention strategy are used to ensure the diversity of the population and accelerate the convergence rate of the algorithm.Then,a variable neighborhood search based on of moving fuzzy critical operation enhances the local search ability of the algorithm.Finally,the effectiveness and feasibility of the model and algorithm are verified by numerical experiments.Four benchmark problems are tested.The results show that the algorithm has certain advantages in terms of accurate solution,robustness and distribution of solution sets,comparing with traditional algorithms.And it is an effective new method for solving fuzzy flexible job shop scheduling problems.