基于高维多目标候鸟优化算法的柔性作业车间调度  

Scheduling of Flexible Job Shop Based on High-dimension and Multi-objective Migrating Bird Optimization Algorithm

作　　者：王秋莲[1] 段星皓 WANG Qiulian;DUAN Xinghao(School of Economics and Management,Nanchang University,Nanchang,330031)

机构地区：[1]南昌大学经济管理学院,南昌330031

出　　处：《中国机械工程》2022年第21期2601-2612,共12页China Mechanical Engineering

基　　金：国家自然科学基金(51765043);江西省社会科学基金(21GL33D)。

摘　　要：针对柔性作业车间调度问题,提出一种改进的多目标候鸟优化算法来求解考虑完工时间、总拖期、机器总负荷以及总能耗的高维多目标问题。多目标候鸟优化算法在候鸟优化算法的基础上引入基于Pareto支配和参考点的选择算子来给予鸟群选择压力,并用基于属性层次模型和灰色关联分析法的组合权重法从最优解集中选择一个最合适的方案。算例和实例验证了算法的有效性和实用性。Aiming at the problems in flexible job shop scheduling,an improved multi-objective migrating bird optimization(MOMBO)algorithm was proposed to solve the high-dimensional multi-objective scheduling problem with the consideration of maximum completion time,total delay period,total load of machine,and total energy consumption.On the basis of migrating bird optimization algorithm,MOMBO algorithm introduced a selection operator based on Pareto domination and reference point to give bird population selection pressure,and the combined weight method based on attribute hierarchical mode and gray relation analysis was used to select the most suitable solution from the optimal solution sets.The effectiveness and practicability of MOMBO were verified by test instances and case study.

关 键 词：柔性作业车间 候鸟优化算法 多目标优化 节能 

分 类 号：TH186[机械工程—机械制造及自动化]