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
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