工艺路线可变的双资源双目标车间调度优化

将遗传算法与启发式调度规则相结合 ,研究了工艺路线可变的双资源双目标的作业车间调度优化问题。在探讨过程中 ,不仅考虑到了每个工件有几条可行的工艺路线 ,而且考虑到了工件的调度受到机床、工人等资源的制约 ,以及在加工过程中发生的储存费用、机床的加工费用和工人的劳动费用对工件调度的影响 ,设计了以生产周期和生产成本综合优化为目标的适应度函数。启发式调度规则使该算法具有较高的局部搜索效率 ,遗传算法保证了解的全局最优性。最后给出了算例 ,并对计算结果进行了分析和讨论

Intelligent Optimization of Bi-objective Job-shop Scheduling Using Genetic Algorithms

The optimization of job shop scheduling is very important because of its theoretical and practical significance. Much research about it have been reported in recent years. But most of them were about classical job shop. The existence of a gap between scheduling theory and practice has been reported in literature. This work presents a robust procedure to solve bi objective job shop scheduling problems with large number of more realistic constraints such as alternative processing plans for parts, requirement of multiple resource to process an operation (machine tools and worker), which simultaneously addresses the reduction of makespan and the costs of operating and storage during parts processing. A combining genetic algorithm(GA) with heuristic scheduling algorithm has been improved, in which the introduction of heuristic rules makes the algorithm faster and more efficient, while GA surely makes the solution a global optimization. An example of scheduling is given, the results show that this method is efficient.