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作 者:李颖俐 刘翱[1] 邓旭东[1] LI Yingli;LIU Ao;DENG Xudong(School of Management,Wuhan University of Science and Technology,Wuhan 430074,China;State Key Lab of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China)
机构地区:[1]武汉科技大学管理学院,湖北武汉430074 [2]华中科技大学数字制造装备与技术国家重点实验室,湖北武汉430074
出 处:《计算机集成制造系统》2024年第11期4087-4098,共12页Computer Integrated Manufacturing Systems
基 金:湖北省自然科学基金青年项目(2023AFB088);智能制造装备与技术全国重点实验室2023年开放课题资助项目(IMETKF2023015);教育部人文社会科学研究规划基金资助项目(21YJAZH050);武汉市知识创新专项项目基础研究资助项目(2022010801010301)。
摘 要:为了优化多车间协同的生产与物流集成调度问题,提出一种多目标人工蜂群算法和优化策略。优化算法采用三层编码表示车间序列、工件序列及机器档位,结合车间分配规则、机器选择策略及自动导引运输车分配规则获得问题可行解。雇佣蜂阶段设计一种基于距离选择的聚类交叉操作,保证种群多样性和解的质量;观察蜂阶段采用了基于关键车间的邻域搜索方法,在庞大解空间中实现高效搜索。侦查蜂阶段基于机器档位和工件运输顺序构建了节能调度策略,丰富非支配解集合。对比经典多目标进化算法,数值实验结果显示所提算法的有效性与优越性。To optimize the integrated production and logistics scheduling problem with multi-shop collaboration,a multi-objective artificial bee colony algorithm and optimization strategy were proposed.The optimization algorithm adopted three-layer encoding method to represent the shop sequence,job sequence and machine speed,and combined the factory allocation rule,machine selection strategy and Automated Guided Vehicle(AGV)allocation rule to obtain a feasible solution to the problem.The employed bee stage designed a clustering crossover operation based on distance selection to ensure population diversity and the quality of solution.The onlooker bee stage adopted a neighborhood search method based on critical factory to achieve efficient search in the huge solution space.The scout bee stage constructed an energy-saving scheduling strategy based on machine speed and the sequence of job transportation to enrich the set of non-dominated solutions.Compared with the classical multi-objective evolutionary algorithm,the numerical experimental results showed the effectiveness and superiority of the proposed algorithm.
关 键 词:分布式异构混合流水车间 自动导引运输车 能耗 人工蜂群算法 多目标优化
分 类 号:TH186[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]
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