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 

机构地区：[1]Hubei Key Laboratory of Modern Manufacturing and Quality Engineering,School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China [2]Hubei Digital Manufacturing Key Laboratory,School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan 430070,China [3]Department of Production Engineering,KTH Royal Institute of Technology Stockholm SE-10044,Sweden

出　　处：《Tsinghua Science and Technology》2024年第5期1390-1408,共19页清华大学学报自然科学版（英文版）

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).

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 solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two 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 algorithm.At 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 search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,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.

关 键 词：flexible job shop scheduling composite dispatching rule improved genetic programming algorithm deep reinforcement learning 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]