LOW-CARBON FLEXIBLE JOB-SHOP SCHEDULING BASED ON IMPROVED NONDOMINATED SORTING GENETIC ALGORITHM-II

被引：24
|
作者：
Seng, D. W. [1 ,2 ]
Li, J. W. [1 ,2 ]
Fang, X. J. [1 ,2 ]
Zhang, X. F. [1 ,2 ]
Chen, J. [1 ,2 ]
机构：
[1] Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Zhejiang, Peoples R China
[2] Minist Educ, Key Lab Complex Syst Modeling & Simulat, Hangzhou 310018, Zhejiang, Peoples R China
来源：
INTERNATIONAL JOURNAL OF SIMULATION MODELLING | 2018年 / 17卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Flexible Job-Shop Scheduling Problem (F[!text type='JS']JS[!/text]P); Nondominated Sorting Genetic Algorithm-II (NSGA-II); Low-Carbon Scheduling; ENERGY-CONSUMPTION; POWER-CONSUMPTION; OPTIMIZATION; FOOTPRINT; MINIMIZATION;
D O I：
10.2507/IJSIMM17(4)CO18
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Considering the impacts of multiple production objectives, makespan and low carbon factor on job-shop scheduling optimization, this paper puts forward a novel low carbon scheduling method for flexible job-shop based on the improved nondominated sorting genetic algorithm-II (NSGA-II). Firstly, a low-carbon scheduling optimization model was established for multi-objective, multi-speed job-shop. Then, the flow of the NSGA-II-based core algorithm was explained, and the new population selection was optimized through the calculation of congestion and nondominated level. Finally, multiple simulation examples were adopted to validate the proposed algorithm. The results show that the proposed NSGA-II low carbon optimization algorithm can converge to the global best Pareto solution rapidly, and lower the no-load and total energy consumption of the production line through automatic management while ensuring production efficiency.
引用
收藏
页码：712 / 723
页数：12
相关论文
共 50 条
  • [21] The Improved Simulated Annealing Genetic Algorithm for Flexible Job-Shop Scheduling Problem
    Gu, Xiaolin
    Huang, Ming
    Liang, Xu
    PROCEEDINGS OF 2017 6TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2017), 2017, : 22 - 27
  • [22] Scheduling for the Flexible Job-Shop Problem Based on Genetic Algorithm(GA)
    Fan, ShunCheng
    Wang, JinFeng
    ADVANCED MATERIALS AND ENGINEERING MATERIALS, PTS 1 AND 2, 2012, 457-458 : 616 - 619
  • [23] An improved genetic algorithm for Job-shop scheduling problem
    Lou Xiao-fang
    Zou Feng-xing
    Gao Zheng
    Zeng Ling-li
    Ou Wei
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 2595 - +
  • [24] Scheduling for the Flexible Job-Shop Problem Based on a Hybrid Genetic Algorithm
    Wang, JinFeng
    Fan, XiaoLiang
    SENSOR LETTERS, 2011, 9 (04) : 1520 - 1525
  • [25] Improved genetic algorithm for the job-shop scheduling problem
    Tung-Kuan Liu
    Jinn-Tsong Tsai
    Jyh-Horng Chou
    The International Journal of Advanced Manufacturing Technology, 2006, 27 : 1021 - 1029
  • [26] Improved genetic algorithm for the job-shop scheduling problem
    Liu, Tung-Kuan
    Tsai, Jinn-Tsong
    Chou, Jyh-Horng
    International Journal of Advanced Manufacturing Technology, 2006, 27 (9-10): : 1021 - 1029
  • [27] An Improved Genetic Algorithm for the Job-Shop Scheduling Problem
    Hong, Hui
    Li, Tianying
    Wang, Hongtao
    DCABES 2008 PROCEEDINGS, VOLS I AND II, 2008, : 621 - +
  • [28] Improved genetic algorithm for the job-shop scheduling problem
    Liu, TK
    Tsai, JT
    Chou, JH
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2006, 27 (9-10): : 1021 - 1029
  • [29] Research on low-carbon flexible job shop scheduling problem based on improved Grey Wolf Algorithm
    Zhou, Kai
    Tan, Chuanhe
    Wu, Yanqiang
    Yang, Bo
    Long, Xiaojun
    JOURNAL OF SUPERCOMPUTING, 2024, 80 (09): : 12123 - 12153
  • [30] Bilevel genetic algorithm for the flexible job-shop scheduling problem
    Zhang, Chaoyong
    Rao, Yunqing
    Li, Peigen
    Shao, Xinyu
    Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering, 2007, 43 (04): : 119 - 124
12345