基于遗传算法的零件多工艺流程生产决策

Genetic Algorithm Based Shop Job Scheduling for Multiple Process Production

针对具有多工艺流程的零件生产,提出采用遗传算法来解决车间生产的排产问题。以满足用户交货期等为约束,以生产成本最低为目标,建立多工艺流程零件排产的决策模型。采用遗传算法对调度求解空间中的加工路径在搜索规则的约束作用下进行遍历,最终寻找到能够满足要求的最优的调度方案。最后,用实例验证了该方法的可行性。

Multiple process parts production allows more selection of machine tools, which also makes job shop scheduling more complex, Aim of this research is to minimize the total production cost in alternative process job shop environment based on Genetic Algorithm（GA）. Decision-making model for Alternate process parts production has been formulated, constrained by the delivery time and other scheduling rules, and the objective is the minimum of production cost, All scheduling schemes in solution space have been searched through genetic algorithm under search rules constraints so as to fmd the optimal scheduling scheme. Finally, a study case has been used to support and validate the proposed model, The result showed that the genetic algorithm was able to find the feasible process routes for a given tasks,