摘要
在综合考虑经济指标和环境因素的基础上,提出了以最小化最大完工时间和总能耗为优化目标的置换流水车间调度问题,并着重考虑学习效应对该问题的影响。针对该问题的特点,构建了带有学习效应的多目标置换流水车间调度模型,并设计了一种嵌有批量处理和扰动算子操作的混合蛙跳算法对模型进行求解。利用扩展的标准测试问题验证了该算法的性能,并与非支配排序遗传算法、强帕累托进化算法进行了对比分析,实验结果表明改进的混合蛙跳算法具有较好的寻优能力。利用此算法得到了不同学习率下的优化目标值,并运用敏感性分析探讨了学习效应对优化目标的影响程度,从而为企业制定合理的生产调度方案提供参考。
Based on comprehensive consideration of economic indicators and environmental factors,a permutation flow-shop scheduling problem with optimization goals of minimizing maximum completion time and total energy consumption is proposed,and the impact of learning effects on this problem is considered.Aiming at the characteristics of this problem,a multi-objective permutation flow shop scheduling model with learning effects was constructed,and a hybrid frog-leaping algorithm with batch processing and perturbation operator operations was designed to solve the model.The performance of the algorithm is verified using extended standard test questions,and compared with non-dominated sorting genetic algorithm and strong Pareto evolution algorithm.The experimental results show that the improved hybrid frog jumping algorithm has better optimization ability.Using this algorithm,the optimization target values under different learning rates are obtained,and the sensitivity analysis is used to explore the impact of learning effects on the optimization goals,so as to provide a reference for companies to formulate a reasonable production scheduling plan.
作者
张洪亮
张金春
盖海江
ZHANG Hongliang;ZHANG Jinchun;GE Haijiang(School of Management Science and Engineering,Anhui University of Technology,Ma'anshan,Anhui 243032,China)
出处
《南华大学学报（自然科学版）》
2020年第5期77-86,共10页
Journal of University of South China：Science and Technology
基金
国家自然科学基金面上项目(71772002)
安徽省高校人文社科重点项目(SK2019A0060)。
关键词
置换流水车间
学习效应
改进的混合蛙跳算法
最大完工时间
总能耗
permutation flow-shop scheduling
learning effects
improved shuffled leapfrog algorithm
maximum completion time
the total energy consumption
