柔性作业车间分批调度多目标优化方法

为了解决柔性作业车间中小批量工件的分批调度多目标优化问题,构建以制造工期、拖期惩罚、加工成本、批次数量和机器总负荷为目标函数的柔性作业车间多目标调度模型.应用改进的强度Pareto进化算法（SPEA）求解.在该算法中,应用模糊c 均值聚类（FCM）加快外部种群的聚类过程,引入自适应的变异算子来增强解的多样性.采用约束Pareto支配和可变长度的编码策略,一次运行就能够求得Pareto最优解集.利用模糊集合理论得到Pareto解的优先选择序列,并从中选出一个最优解.该方法将工件分割成具有柔性数量的多个批次,使各批次的工艺路线选取及加工顺序得到优化.通过实例仿真对该方法的性能进行比较分析.将该方法应用于某机械公司车间调度中,验证了该方法的有效性和适应性.

Multi-objective optimization method of flexible job-shop lot-splitting scheduling

The multi objective flexible job-shop scheduling optimization model was conducted in order to solve multi-objective optimization problem of flexible job-shop small batch lot splitting scheduling. The model was concerned with makespan, tardiness penalty, manufacturing cost, count of batch and total workload. The optimal solutions were obtained by using improved strength Pareto evolutionary algorithm (SPEA). The algorithm was improved by using the fuzzy c-means clustering algorithm to accelerate the clustering procedure within the external population. A self-adaptive mutation operator was also introduced to enhance the diversity of solutions. A Pareto optimal set can be achieved in a single run with the constraint Pareto domination concept and the flexible representation schema. Then the preference sequence of Pareto solutions was achieved and a solution was extracted as the best compromise one based on set theory. The jobs were split into flexible size batch, and the batch routing and sequencing were simultaneously optimized by the method. The performance of the method was evaluated through simulation. The feasibility and validity of the method were proved in a workshop scheduling.