Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

作     者：Xixing Li Qingqing Zhao Hongtao Tang Xing Guo Mengzhen Zhuang Yibing Li Xi Vincent Wang 

作者机构：Hubei Key Laboratory of Modern Manufacturing and Quality EngineeringSchool of Mechanical EngineeringHubei University of TechnologyWuhan 430068China Hubei Digital Manufacturing Key LaboratorySchool of Mechanical and Electronic EngineeringWuhan University of TechnologyWuhan 430070China Department of Production EngineeringKTH Royal Institute of Technology Stockholm SE-10044Sweden 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报自然科学版（英文版）)

年 卷 期：2024年第29卷第5期

页      面：1390-1408页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 12[管理学] 120501[管理学-图书馆学] 120502[管理学-情报学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.51805152 and 52075401) the Green Industry Technology Leading Program of Hubei University of Technology(No.XJ2021005001) the Scientific Research Foundation for High-level Talents of Hubei University of Technology(No.GCRC2020009) the Natural Science Foundation of Hubei Province(No.2022CFB445) 

主　　题：flexible job shop scheduling composite dispatching rule improved genetic programming algorithm deep reinforcement learning 

摘      要：To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible ***,the binary tree coding method is adopted,and the constructed function set is ***,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is *** population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the *** the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local *** addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative ***,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.