带分时电价约束的分布式柔性流水车间调度问题及其求解算法  

Distributed flexible flow-shop scheduling problem with time-of-use electricity tariffs constraint and its solving algorithm

作　　者：许天鹏 赵付青[1] 张建林[1] 王维元 杜松霖 XU Tianpeng;ZHAO Fuqing;ZHANG Jianlin;WANG Weiyuan;DU Songlin(College of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China)

机构地区：[1]兰州理工大学计算机与通信学院,甘肃兰州730050 [2]上海大学机电工程与自动化学院,上海200444

出　　处：《计算机集成制造系统》2025年第4期1299-1313,共15页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(62063021);甘肃省自然科学基金资助项目(24JRRA178);甘肃省高端外国专家引进计划资助项目(22JR10KA007);甘肃省青年科技基金计划资助项目(22JR5RA311)。

摘　　要：能源成本和生产效率是智能制造的关键,为了在降低电力成本的同时提升生产效率,以分布式制造环境下的柔性流水车间调度问题作为研究对象(DFFSP),重点分析了分布式柔性流水车间调度问题的特性,考虑分时电价(TOU)约束,以最小化最大完工时间和总电力成本为优化指标,建立了DFFSP-TOU问题整数规划模型,根据分时电价下分布式柔性流水车间调度问题特性DFFSP-TOU,提出一种基于自学习机制的多目标帝王蝶优化算法(MOLMBO)。算法的迁移算子和调整算子通过历史最优解的信息自学习生成,以增强该算法的自学习、自适应能力;采用变邻域搜索来提高算法的局部搜索性能和种群多样性;通过右移操作将电价区间在高峰时段的生产转移到电价区间在低谷时段进行生产,减少机器在待机状态下的能耗,进而降低电力成本。实验结果表明MOLMBO算法是求解分布式柔性流水车间调度问题的一种有效的方法。Energy costs and production efficiency are key factors in smart manufacturing.In order to reduce electricity costs while improving production efficiency,by taking the Distributed Flexible Flow-shop Scheduling Problem(DFFSP)as the objective,the characteristics of DFFSP were analyzed.Considering the constraint of the Time-of-Use(TOU)electricity tariffs,an integer programming model of DFFSP-TOU was formulated with the objectives of minimizing makespan and total energy consumption.A Multi-objective Learning Monarch Butterfly Optimization algorithm(MOLMBO)based on self-learning mechanism was proposed according to the characteristics of DFFSP-TOU,in which the migration operator and adjusting operator were generated by the information of historical optimal solutions to enhance self-learning and the adaptive ability.Furthermore,the variable neighborhood search strategy was used to improve the performance of local search and enhance the diversity of population.In addition,the right-shift operation was applied to transfer the production from the peak times to reduce the energy consumption.The performance of the proposed algorithm was verified on benchmark problems,the experimental results showed that the MOLMBO was an effective method in addressing DFFSP.

关 键 词：分时电价 分布式柔性流水车间调度 多目标优化算法 帝王蝶优化算法 学习机制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]