Improved NSGA- II algorithm for hybrid flow shop scheduling problem with multi-objective

被引：0
|
作者：
Song C. [1 ,2 ]
机构：
[1] College of Software, Dalian Jiaotong University, Dalian
[2] Artificial Intelligence Key Laboratory of Sichuan Province, Zigong
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 06期
关键词：
fast elitist non-dominated sorting genetic algorithm; hybrid flow shop scheduling; minimum energy consumption; minimum makespan; multi-objective optimization;
D O I：
10.13196/j.cims.2022.06.016
中图分类号：
学科分类号：
摘要：
Aiming at the hybrid flow shop scheduling problem, a Mixed Integer Linear Programming (MILP) model was set up by taking the makespan and minimum energy consumption as the solving objectives, and an improved multi-objective NSGA II algorithm was proposed to solve it. A novel chromosome coding method was put forward, which could ensure NSGA- II algorithm search the whole solution space. Three decoding methods were designed, and two of them were close related with the objectives of the problem and guide the algorithm's search direction. A greedy mutation operator was used here to improve algorithm's local searching ability and an improved choice operator was proposed to ensure the variety of chromosome and avoid premature convergence. To reduce the energy consumption, an mobile strategy was proposed to cut down the standby energy consumption and turning-on/off energy consumption. Numerical experiments were carried out to evaluate the performance and efficiency of the proposed approach. © 2022 CIMS. All rights reserved.
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页码：1777 / 1789
页数：12
相关论文
共 28 条
  • [1] WU X, SUN Y., A green scheduling algorithm for flexible job shop with energy-saving measures[J], Journal of Cleaner Production, 172, pp. 3249-3264, (2018)
  • [2] WANG II, JIANG Z, WANG Y, Et al., A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization, Journal of Cleaner Production, 188, pp. 575-588, (2018)
  • [3] MENG Leilei, ZHANG Chaoyong, SIIAO Xinyu, Et al., Mathematical modeling of energy-efficient integration of process planning and scheduling, Journal of Mechanical Engineering, 55, 16, pp. 186-196, (2019)
  • [4] REN Caile, YANG Xudong, ZHANG Chaoyong, Et al., Modeling and optimization for energy-efficient hybrid flow-shop scheduling problem [J], Computer Integrated Manufacturing Systems, 25, 8, pp. 1965-1980, (2019)
  • [5] SONG Cunli, An improved greedy genetic algorithm for solving the hybrid flow-shop scheduling problem [J], Systems Engineering and Electronics, 41, 5, pp. 148-155, (2019)
  • [6] CUI Qi, WU Xiuli, YU Jianjun, Improved genetic algorithm variable neighborhood search for solving hybrid flow shop scheduling problem [J], Computer Integrated Manufacturing Systems, 23, 9, pp. 1917-1927, (2017)
  • [7] SHI Weiguo, SONG Cunli, Improvedgrey wolf optimizationto solve the hybrid flow shop scheduling problem with identical parallel machines, Computer Integrated Manufacturing Systems, 27, 11, pp. 3196-3208, (2021)
  • [8] LOW C., Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines[J], Computers and Operations Research, 32, 8, pp. 2013-2025, (2005)
  • [9] ESKANDARI H, HOSSEINZADEIH A., A variable neighborhood search for hybrid flow-shop scheduling problem with rework and set-up times, Journal of the Operational Research Society, 65, 8, pp. 1221-1231, (2014)
  • [10] LI J Q, PAN Q K, WANG F T., A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem, Applied Soft Computing, 24, pp. 63-77, (2014)
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