A Multi-Objective Optimization Model Based on Non-Dominated Sorting Genetic Algorithm
作者: Fu, H. C.Liu, P.
刊名: International Journal of Simulation Modeling, 2019, Vol.18 (3), pp.510-520
来源数据库: DAAAM International Vienna
DOI: 10.2507/IJSIMM18(3)CO12
关键词: Job-Shop Scheduling Problem (JSP)Genetic Algorithm (GA)Non-Dominated Sorting Genetic Algorithm (NSGA)Multi-Objective Scheduling
原始语种摘要: This paper attempts to solve the job-shop scheduling problem (JSP), in which machines are shared among multiple tasks. For this purpose, a multi-objective optimization model was established to minimize the total completion time and total cost. To solve the model, a scheduling strategy was proposed based on the NGSA with crowding mechanism. Compared with the GA, the improved NGSA can effectively avoid the local optimum trap and maintain population diversity in the later stage. In addition, the heuristic crossover operator was introduced to enhance the local search ability of the improved NGSA. The effectiveness of the proposed scheduling strategy was proved valid through simulation.
全文获取路径: 国际维也纳多瑙河亚德里亚自动化与制造协会 
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关键词翻译
关键词翻译
  • scheduling 
  • Sorting 分类
  • minimize 最小化
  • solve 
  • crossover 交叉
  • optimization 最佳化
  • improved 改进
  • heuristic 试探
  • shared 共享
  • effectiveness 有效性