改进自适应遗传算法在多载AGV调度的应用研究  

Research on Application of Improved Adaptive Genetic Algorithm in Multi-load AGV Scheduling

作　　者：刘畅 张承瑞[1,2] 孙玉玺[1,2] LIU Chang;ZHANG Cheng-rui;SUN Yu-xi(School of Mechanical Engineering,Shandong University,Jinan 250061,China;Key Laboratory of High Efficiency and Clean Mechanical Manufacture,Shandong University,Jinan 250061,China)

机构地区：[1]山东大学机械工程学院,济南250061 [2]山东大学高效清洁机械制造教育部重点实验室,济南250061

出　　处：《小型微型计算机系统》2021年第11期2241-2245,共5页Journal of Chinese Computer Systems

基　　金：2018年山东省重点研发计划项目(重大科技创新工程)(2018CXGC0903)资助.

摘　　要：随着工业4.0的推进,如何进行高效的AGV调度成为研究热点.针对传统AGV调度问题只考单一负载的情况,以及标准遗传算法在求解调度问题中,存在收敛速度慢,容易陷入局部极值等缺点的现象.提出一种改进的自适应遗传算法.该算法引入逆转算子、插入算子以及灾变算子,并对其进行自适应控制,算法的逆转过程有利于进化方向的搜索.模型在AGV调度问题基础上,添加了物料重量、体积双重约束,以最短路径成本为目标函数,建立多载AGV路径规划模型.然后,对数据进行预处理,使用改进的自适应遗传算法进行求解.该算法在MATLAB平台上进行算例验证,与标准遗传算法、自适应遗传算法比较,证明了改进的自适应遗传算法收敛快、求解结果更接近最优解.With the advancement of Industry 4.0,how to schedule AGV efficiently has become a research hotspot.Because the traditional AGV scheduling problem only considers the single load,and the standard genetic algorithm has the disadvantages of slow convergence speed and easy to fall into local extremes,An improved adaptive genetic algorithm(IAGA)is proposed.The algorithm introduces inversion operators,insertion operators and catastrophe operators,and performs adaptive control on them.The inversion process of the algorithm is conducive to the search for the evolution direction.Based on the AGV scheduling problem,the model adds the dual constraints of material weight and volume,and takes the shortest path cost as the objective function to establish a multi-load AGV path planning model.Then,the data is preprocessed and solved using the improved adaptive genetic algorithm.The algorithm is verified on the MATLAB platform.Compared with the standard genetic algorithm(SGA)and adaptive genetic algorithm(AGA),it proves that the improved adaptive genetic algorithm converges quickly and the solution result is closer to the optimal solution.

关 键 词：多载AGV 调度 数据预处理 自适应遗传算法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]