蚁群优化算法
计算机科学
作业车间调度
数学优化
调度(生产过程)
稳健性(进化)
建设性的
计算
元启发式
蚁群
分布式计算
人工智能
过程(计算)
算法
数学
地铁列车时刻表
生物化学
化学
基因
操作系统
作者
Jun Zhang,Xiao-Min Hu,Xuan Tan,Jiaqi Zhong,Qiong Huang
标识
DOI:10.1191/0142331206tm165oa
摘要
Research on optimization of the job shop scheduling problem (JSP) is one of the most significant and promising areas of optimization. Instead of the traditional optimization method, this paper presents an investigation into the use of an Ant Colony System (ACS) to optimize the JSP. The main characteristics of this system are positive feedback, distributed computation, robustness and the use of a constructive greedy heuristic. In this paper, an improvement of the performance of ACS will be discussed. The numerical experiments of ACS were implemented in a small JSP. The optimized results of the ACS are favourably compared with the traditional optimization methods.
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