采用AGV分拣的型材下料车间成组调度问题研究  

Group scheduling problem of profile blanking workshop using AGV sorting

作　　者：汤洪涛[1] 郑之恒 李英德[1] 陈青丰 江伟光[1] TANG Hongtao;ZHENG Zhiheng;LI Yingde;CHEN Qingfeng;JIANG Weiguang(College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]浙江工业大学机械工程学院,浙江杭州310023

出　　处：《计算机集成制造系统》2023年第1期100-110,共11页Computer Integrated Manufacturing Systems

基　　金：国家重点研发计划资助项目(2018YFB1308100);浙江省自然科学基金资助项目(LY18G020018)。

摘　　要：针对一种基于自动导引小车(AGV)分拣的型材下料车间分拣新方法,以最小化AGV损耗费用和AGV运行费用为目标,建立了混合整数线性规划模型,设计了一种改进遗传算法对模型进行求解。该算法使用带加工属性的多层编码方式,针对多层编码设计了分层式交叉变异的方式,在邻域搜索阶段采用基于禁忌表的双层协同优化策略。算例对比实验表明,所设计的改进遗传算法与基础遗传算法、基础蚁群算法、变邻域改进遗传算法,以及改进蜂群算法相比,在求解该问题上有显著优势和有良好的鲁棒性。Aiming at a new sorting method of profile blanking workshop based on Automated Guided Vehicle(AGV) sorting, a mixed integer linear programming model was established with the goal of minimizing AGV loss cost and AGV operation cost, and an improved genetic algorithm was designed to solve the model. In this algorithm, used a multi-layer encoding method with processing attributes was used, and a hierarchical cross-mutation method for multi-layer coding was designed. In the neighborhood search stage, a two-layer collaborative optimization strategy based on tabu tables was adopted. The comparative experiments of numerical examples showed that the proposed improved genetic algorithm had significant advantages and good robustness in solving the problem compared with the basic genetic algorithm, the basic ant colony algorithm, the variable neighborhood improved genetic algorithm, and the improved bee colony algorithm.

关 键 词：自动导引小车分拣 型材下料车间 成组调度 遗传算法 大规模实例生产应用 禁忌表 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]