[1]陈心文,温廷羲,傅玉青,等.改进的NSGA-Ⅱ算法求解绿色柔性作业车间调度问题[J].华侨大学学报(自然科学版),2025,46(5):551-560.[doi:10.11830/ISSN.1000-5013.202508028]
　CHEN Xinwen,WEN Tingxi,FU Yuqing,et al.Improved NSGA-Ⅱ Algorithm for Solving Green Flexible Job-Shop Scheduling Problem[J].Journal of Huaqiao University(Natural Science),2025,46(5):551-560.[doi:10.11830/ISSN.1000-5013.202508028]
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改进的NSGA-Ⅱ算法求解绿色柔性作业车间调度问题()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第46卷
期数:
2025年第5期
页码:
551-560
栏目:
出版日期:
2025-09-20

文章信息/Info

Title:
Improved NSGA-Ⅱ Algorithm for Solving Green Flexible Job-Shop Scheduling Problem
文章编号:
1000-5013(2025)05-0551-10
作者:
陈心文1;  温廷羲1;  傅玉青1;  许剑飞2
1. 华侨大学 工学院, 福建 泉州 362021;2. 信泰(福建)科技有限公司, 福建 泉州 362200
Author(s):
CHEN Xinwen1;  WEN Tingxi1;  FU Yuqing1;  XU Jianfei2
1. College of Engineering, Huaqiao University, Quanzhou 362021, China; 2. SinceTech(Fujian)Technology Limited Company, Quanzhou 362200, China
关键词:
绿色柔性作业车间调度问题;  NSGA-Ⅱ;  多目标优化;  变邻域搜索
Keywords:
green flexible job-shop scheduling problem;  NSGA-Ⅱ;  multi-objective optimization;  variable neighborhood search
分类号:
TH165
DOI:
10.11830/ISSN.1000-5013.202508028
文献标志码:
A
摘要:
针对绿色柔性作业车间调度问题,以最小化最大完工时间和总能耗为优化目标,建立多目标调度优化模型,提出一种改进的非支配排序遗传算法(INSGA-Ⅱ)。首先,采用混合初始化策略生成高质量且多样化的初始种群;其次,引入自适应交叉变异概率和改进的精英选择策略,避免早熟收敛;然后,提出一种改进的变邻域搜索策略以增强局部搜索精度;最后,在Brandimarte和Hurink数据集的14个标准测试案例上,将INSGA-Ⅱ与NSGA-Ⅱ、NSGA-Ⅲ和SPEA2进行对比。结果表明:INSGA-Ⅱ在解集的收敛性、多样性方面均表现出显著优势,能够有效求解绿色柔性作业车间调度问题,为绿色制造提供了新的解决方案。
Abstract:
For the green flexible job-shop scheduling problem, a multi-objective scheduling optimization model is established with the optimization objectives of minimizing the makespan and total energy consumption. An improved non-dominated sorting genetic algorithm(INSGA-Ⅱ)is proposed to solve the model. First, a hybrid initialisation strategy is adopted to generate a high-quality and diverse initial population. Second, adaptive crossover and mutation probabilities, together with an improved elite selection strategy, are introduced to avoid premature convergence. Then, an enhanced variable neighbourhood search strategy is developed to enhance the local search accuracy. Finally,the proposed INSGA-Ⅱ is compared with NSGA-Ⅱ, NSGA-Ⅲ, and SPEA2 on 14 standard test cases from the Brandimarte and Hurink datasets. Experimental results show that INSGA-Ⅱ demonstrates significant advantages in the convergence and diversity of the solution set, and can effectively solve the green flexible job-shop scheduling problem, providing a novel solution for green manufacturing.

参考文献/References:

[1] ZHANG Jian,DING Guofu,ZOU Yisheng,et al.Review of job shop scheduling research and its new perspectives under Industry 4.0[J].Journal of Intelligent Manufacturing,2019,30(4):1809-1830.DOI:10.1007/s10845-017-1350-2.
[2] TENG Guodong.An improved genetic algorithm for dual-resource constrained flexible job shop scheduling problem with tool-switching dependent setup time[J].Expert Systems with Applications,2025,281:127496.DOI:10.1016/J.ESWA.2025.127496.
[3] AMJAD M K,BUTT S I,KOUSAR R,et al.Recent research trends in genetic algorithm based flexible job shop scheduling problems[J].Mathematical Problems in Engineering,2018,2018:9270802.DOI:10.1155/2018/9270802.
[4] 崔雪艳,万烂军,赵昊鑫,等.基于深度强化学习的柔性作业车间调度方法[J].制造技术与机床,2023(12):165-170.DOI:10.19287/j.mtmt.1005-2402.2023.12.024.
[5] 陈永灿,刘宇,周艳平.求解多目标柔性作业车间调度问题的混合自适应差分进化算法[J].制造技术与机床,2023(12):171-177.DOI:10.19287/j.mtmt.1005-2402.2023.12.025.
[6] MASMOUDI O,DELORME X,GIANESSI P.Job-shop scheduling problem with energy consideration[J].International Journal of Production Economics,2019,216:12-22.DOI:10.1016/j.ijpe.2019.03.021.
[7] MOKHTARI H,HASANI A.An energy-efficient multi-objective optimization for flexible job-shop scheduling problem[J].Computers & Chemical Engineering,2017,104:339-352.DOI:10.1016/j.compchemeng.2017.05.004.
[8] WU Xiuli,SUN Yangjun.A green scheduling algorithm for flexible job shop with energy-savingmeasures[J].Journal of Cleaner Production,2018,172:3249-3264.DOI:10.1016/j.jclepro.2017.10.342.
[9] 杨国栋,冯国红.改进麻雀算法求解低碳柔性作业车间调度问题[J].中国新技术新产品,2023(20):131-133.DOI:10.13612/j.cnki.cntp.2023.20.003.
[10] 徐宜刚,陈勇,王宸,等.改进NSGA-Ⅲ求解高维多目标绿色柔性作业车间调度问题[J].系统仿真学报,2024,36(10):2314-2329.DOI:10.16182/j.issn1004731x.joss.23-0694.
[11] JIANG Tianhua,DENG Guanlong.Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption[J].IEEE Access,2018,6:46346-46355.DOI:10.1109/access.2018.2866133.
[12] LUAN Fei,ZHAO Hongxuan,LIU Shiqiang,et al.Enhanced NSGA-Ⅱ for multi-objective energy-saving flexible job shop scheduling[J].Sustainable Computing: Informatics and Systems,2023,39:100901.DOI:10.1016/J.SUSCOM.2023.100901.
[13] PAN Zixiao,LEI Deming,WANG Ling.A bi-population evolutionary algorithm with feedback for energy-efficient fuzzy flexible job shop scheduling[J].IEEE Transactions on Systems, Man, and Cybernetics: Systems,2021,52(8):5295-5307.DOI:10.1109/TSMC.2021.3120702.
[14] LUAN Fei,LI Ruitong,LIU Shiqiang,et al.An improved sparrow search algorithm for solving the energy-saving flexible job shop schedulingproblem[J].Machines,2022,10(10):847.DOI:10.3390/MACHINES10100847.
[15] SANG Yanwei,TAN Jianping.Many-objective flexible job shop scheduling problem with green consideration[J].Energies,2022,15(5):1884.DOI:10.3390/EN15051884.
[16] LI Jian,LI Huankun,HE Pengbo,et al.Flexible job shop scheduling optimization for green manufacturing based on improved multi-objective wolf packalgorithm[J].Applied Sciences,2023,13(14):8535.DOI:10.3390/APP1314 8535.
[17] LUAN Fei,CAI Zongyan,WU Shuqiang,et al.Optimizing the low-carbon flexible job shop scheduling problem with discrete whale optimization algorithm[J].Mathematics,2019,7(8):688.DOI:10.3390/math7080688.
[18] LI Zhi,CHEN Yingjian.Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects[J].Scientific Reports,2023,13(1):6369.DOI:10.1038/S41598-023-33615-Z.
[19] PENG Zhao,ZHANG Huan,TANG Hongtao,et al.Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect[J].Journal of Intelligent Manufacturing,2021,33(6):1-22.DOI:10.1007/S10845-020-01713-8.
[20] DEB K,AGRAWAL S,PRATAP A,et al.A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.DOI:10.1109/4235.996017.
[21] DEB K,JAIN H.An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part Ⅰ: Solving problems with box constraints[J].IEEE Transactions on Evolutionary Computation,2013,18(4):577-601.DOI:10.1109/TEVC.2013.2281535.
[22] ZITZLER E,LAUMANNS M,THIELE L,et al.SPEA2: Improving the strength Pareto evolutionary algorithm[J].TIK report,2001,103.DOI:10.3929/ethz-a-004284029.
[23] BRANDIMARTE P.Routing and scheduling in a flexible job shop by tabu search[J].Annals of Operations Research,1993,41(3):157-183.DOI:10.1007/BF02023073.
[24] HURINK J,JURISCH B,THOLE M.Tabu search for the job-shop scheduling problem with multi-purpose machines[J].Operations-Research-Spektrum,1994,15(4):205-215.DOI:10.1007/BF01719451.

备注/Memo

备注/Memo:
收稿日期: 2025-08-29
通信作者: 温廷羲(1986-),男,副教授,博士,主要从事大数据分析、深度学习、数据挖掘、生物信息学及物联网技术应用的研究。 E-mail:t.wen@hqu.edu.cn。
基金项目: 福建省泉州市科技计划项目(2024QZC010R, 2024G11)https://hdxb.hqu.edu.cn/
更新日期/Last Update: 2025-09-20