改进蝙蝠算法求解多目标混合车间调度问题  

Improving Bat Algorithm to Solve Multi Objective Hybrid Workshop Scheduling Problems

作　　者：李轩 李仁旺[1] LI Xuan;LI Renwang(School of Mechanical Engineering,Zhejiang University of Science and Technology,Hangzhou310018,China)

机构地区：[1]浙江理工大学机械工程学院,浙江杭州310018

出　　处：《轻工机械》2025年第1期98-104,共7页Light Industry Machinery

摘　　要：针对混合车间调度问题(Hybrid Flowshop Scheduling Problem,HFSP)求解规模大、易陷入局部最优等,笔者提出了一种改进蝙蝠算法(Improved Bat Algorithm,IBA)。以最小化总完工时间、最小化总能耗和平衡机器负载为目标函数,算法中加入了基于指数递减策略的动态惯性权重,并结合包括自适应参数调整、混合局部搜索以及全局搜索策略等多种优化策略,以提高调度效率和优化调度结果。笔者将改进蝙蝠算法与遗传算法(Genetic Algorithm,GA)和蝙蝠算法(Bat Algorithm,BA)进行了对比实验,结果表明:改进蝙蝠算法策略合理有效,且在求得最优解时表现更好。In order to solve the Hybrid Flowshop Scheduling Problem(HFSP)with large scale of solution and easy to fall into local optimum,an Improved Bat Algorithm(IBA)was proposed.Focusing on minimizing total completion time,minimizing energy consumption,and balancing machine loads as objective functions,the dynamic inertia weight based on exponential decreasing strategy was added to the algorithm,and various optimization strategies including adaptive parameter adjustment,hybrid local search and global search strategy were combined to improve the scheduling efficiency and optimize the scheduling results.Comparative studies were conducted using Improved Bat Algorithm(IBA),Genetic Algorithm(GA)and Bat Algorithm(BA).The results show that IBA is reasonable and effective,and demonstrate superior performance in finding optimal solutions while enhancing scheduling efficiency.

关 键 词：调度 混合车间 改进蝙蝠算法 自适应参数 局部搜索 动态惯性权重 

分 类 号：TH186[机械工程—机械制造及自动化] TH164