Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm  

作　　者：WANG Cuiyu LI Yang LI Xinyu 

机构地区：[1]School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Journal of Systems Engineering and Electronics》2021年第2期261-271,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Key R&D Program of China(2018AAA0101700);the Program for HUST Academic Frontier Youth Team(2017QYTD04).

摘　　要：The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

关 键 词：flexible job shop scheduling problem(FJSP) collaborative genetic algorithm co-evolutionary algorithm 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH165[自动化与计算机技术—控制科学与工程]