SOLVING FUZZY FLEXIBLE JOB SHOP SCHEDULING PROBLEMS USING GENETIC ALGORITHM

摘要：

This paper presents a two-population genetic algorithm (TPGA) for FfJSSPs with the maximum fuzzy completion time. TPGA uses two-string representation to represent a solution and two populations to search the optimal schedule. In each generation, crossover and mutation are only applied to one part of the chromosome and these populations are combined and updated by using half of the individuals with the bigger fitness in the combined population. Some instances of FfJSSP are designed and the performance of TPGA is tested. The computational results demonstrate the promising performance of TPGA on FfJSSP.

关键词： Fuzzy processing time Flezible job shop scheduling Genetic algorithm

作者: DE-MING LEI XIU-PING GUO

作者单位: School of Automation, Wuhan University of Technology, Wuhan, Hubei Province School of Economic and Management, Southwest Jiaotong University, Chengdu, Sichuan Province, PR Chin

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1014-1019

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）