基于准时制的时间成本双目标作业调度优化

Intelligent scheduling optimization of bi-objective job-shop based JIT production

提出了一种基于混合遗传算法的以生产周期和生产成本为优化目标的作业调度方法 .该方法采用Giffler Thompson启发式调度算法产生活动的调度 ,基于工序编码的染色体决定了工序调度的优先级 ,在启发式调度算法产生的冲突集合中 ,根据工序的优先级选择下一步安排加工的工序 .混合遗传运算在全局范围内搜索具有最优调度工序优先级的染色体 .同时 ,在Giffler Thompson的启发式算法中 ,采用了反向调度的策略 ,即从工件的交货期开始 ,先安排最后一道生产工序 ,然后依次安排前一道生产工序 ,直到工件的第一道工序调度完毕 ,形成一个完整的调度方案 .在算法中 ,不仅考虑了工件的生产周期和多个工艺计划 ,而且考虑了库存费用和加工费用 ,设计了基于生产周期和生产成本的双目标适应度函数 .

A robust procedure is presented to solve bi-objective scheduling problems based on just-in-time (JIT) production with large number of more realistic constrains such as alternative processing plans for parts, which simultaneously addresses the reduction of make span and the costs of operating and storage during parts processing. Genetic algorithm combined with heuristic scheduling is applied for job shop problems. The conflict among the contending jobs in the Giffler and Thompson procedure is resolved and the optimal operations precedence is derived. Backward scheduling is used to construct a reversed problem for a forward scheduling problem with precedence constraints by considering the predecessors of each operation in the forward problem as successors in the reverse problem. The release time of each job is assured to equate to its due date. An example of scheduling is given, and the results show that the method is available and efficient.