动态柔性作业车间优化调度与决策方法

针对多目标动态柔性作业车间调度问题,提出一种改进的多目标差分进化算法进行求解。在差分进化算法中引入自适应交叉变异算子,提高算法的全局搜索能力;在选择排序时引入基于免疫学原理的快速非支配排序法,提高解集的质量。提出改进的TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution)—G1—熵权综合决策方法。通过Nash均衡理论计算得出G1—熵权法的综合权重;将综合权重与TOPSIS评价体系组合对各调度方案进行评价。通过实验仿真验证了优化调度算法在寻优能力上的优越性以及综合决策方法的有效性。

Optimal Scheduling and Decision Making Method for Dynamic Flexible Job Shop

For multi-objective dynamic flexible job-shop scheduling, an improved multi-objective differential evolution algorithm is proposed. The adaptive cross-mutation operator is introduced into the differential evolution algorithm to improve its global search capability. The fast non-dominated sorting method based on immunological principles is introduced to improve the quality of the solution set in the selection and sorting. An improved TOPSIS-G1-EVM comprehensive decision-making method is proposed. The comprehensive weight of G1-EVM is calculated by Nash equilibrium theory. The comprehensive weight and TOPSIS evaluation system are combined to evaluate each dispatching scheme. The experimental results show that the optimal scheduling algorithm is superior in the optimization ability and the effectiveness of the comprehensive decision-making method.