Abstract: In order to optimize the integrated production and logistics scheduling problem with multi-shop collaboration,a multi-objective artificial bee colony algorithm and optimization strategy are proposed.The optimization algorithm adopts three-layer encoding method to represent the shop sequence,job sequence and machine speed,and combines the factory allocation rule,machine selection strategy and Automated Guided Vehicle allocation rule to obtain a feasible solution to the problem.The employed bee stage designs a clustering crossover operation based on distance selection to ensure population diversity and the quality of solution.The onlooker bee stage adopts a neighborhood search method based on critical factory to achieve efficient search in the huge solution space.The scout bee stage constructs 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 show the effectiveness and superiority of the proposed algorithm.

Key words: distributed heterogeneous hybrid flow shop, automated guided vehicle, energy consumption, artificial bee colony algorithm, multi-objective optimization

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。

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