An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

作     者：WU Xiuli PENG Junjian XIE Zirun ZHAO Ning WU Shaomin WU Xiuli;PENG Junjian;XIE Zirun;ZHAO Ning;WU Shaomin

作者机构：School of Mechanic EngineeringUniversity of Science and Technology BeijingBeijing 100083China Kent Business SchoolUniversity of KentKent CT27FSUK 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2021年第32卷第2期

页      面：272-285页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080202[工学-机械电子工程] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key R&D Plan(2020YFB1712902) the National Natural Science Foundation of China(52075036) 

主　　题：flexible job shop variable batch inverse scheduling multi-objective evolutionary algorithm based on decomposition a batch optimization algorithm with inverse scheduling 

摘      要：In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse ***,a flexible job shop problem with the variable batches scheduling model is ***,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting ***,in order to increase the diversity of the population,two methods are *** is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the ***,a group of experiments are carried *** results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.