基于混合遗传算法的生产工艺规划与决策问题

摘要

In part processing,due to various processing features,different processes,and process-constrained processing sequencing rules,there are large number of processes and machine choices.Thus,it is known that flexible process planning belongs to NP-hard problems.By using segment coding for process and machine selection,a constrained adjustment algorithm is designed to solve the process-constrained processing sequencing problem.With multiple objectives for the problem,random weights are generated to configure the fitness function.External elitist strategy is used and a K-means clustering algorithm is set to clip elite sets in order to keep the diversity of population.In this way,hybrid genetic algorithm is presented by designing crossover and mutation operation.The proposed algorithm can effectively solve the problem of multiple process optimization and decision problem with process constraints.Finally,a real case study is used to verify its effectiveness for solving flexible process planning issues.%零件生产加工过程中,由于各加工特征有多个加工工艺而不同工艺方法又有不同的机器选择,以及受工艺约束的工序特征排序问题,使得柔性工艺规划问题具有NP难特性.通过对可选工序和机器进行分段编码；并用约束调整算法解决受工艺约束的工序排序问题；对于问题的多目标特性,采用随机权重来设置适应度函数,用外部精英保留策略并引入k-means聚类算法裁剪精英集来保持群体多样性,该方法通过该混合遗传算法的交差,变异等操作,能有效解决受工序约束的多工艺路线的优化与决策问题.以实例的形式论证了该算法在求解柔性工艺规划问题的有效可行性.