能耗和噪声约束下的柔性车间调度决策优化

针对柔性作业车间生产过程中能源消耗和噪声污染严重的问题,在考虑最大完工时间的基础上,将能耗和噪声作为独立的决策变量,构建关于完工时间、能耗和噪声的多目标FJSP优化模型,并改进鲸鱼算法实现调度优化.首先,利用转换序列二段式编码方式将连续个体映射为离散个体并通过反向学习法初始化种群,提高算法的搜索性能;其次,在种群迭代过程中采用收敛因子非线性调整策略,并基于小生境技术对存储非劣解的外部文档进行更新,结合二次插值变异算子,避免算法陷入早熟收敛;最后,通过评价系数的权重选出Pareto解集中的满意解.针对具体实例进行测试,证明所提算法的可行性和有效性.

Flexible Shop Scheduling Decision Optimization Under the Constraint of Energy Consumption and Noise

In view of the serious problems of energy consumption and noise pollution in the process of flexible workshop production,on the basis of considering the maximum time to completion,energy consumption and noise as independent decision variables,a multiobjective FJSP optimization model about time to completion,energy consumption and noise was built,and the whale algorithm was improved to achieve scheduling optimization. Firstly,continuous individuals are mapped to discrete individuals by two-stage coding method of transformation sequence,and the population is initialized by reverse learning method to improve the search performance of the algorithm.Secondly,in the population iteration process,the non-linear adjustment strategy of convergence factor is adopted,and the external documents storing non-inferior solutions are updated based on niche technology,and the quadratic interpolation mutation operator is combined to avoid the algorithm falling into premature convergence. Finally,the satisfactory solution of Pareto solution set is selected by the weight of the evaluation coefficient. The feasibility and effectiveness of the proposed algorithm are proved by a practical example.