A Cooperative Coevolutionary Genetic Scheduling Algorithm for solving stochastic Job Shop scheduling

摘要：

Coevolutionary algorithms have gained much attention in the past few years for its powerful searching ability.In this paper,we combine the coevolutionary computation with genetic algorithm,introducing a novel algorithm—Cooperative Coevolutionary Genetic Scheduling Algorithm (CCGSA) for solving the stochastic Job Shop Scheduling Problem (JSSP).In CCGSA,the number of sub-population depends on the number of working procedures.The interaction of all sub-populations is reflected by fitness function.The global optimization can be obtained with probability 1 with interaction of mutation and crossover operator in each sub-population.Based on stochastic sampling simulation and stochastic programming theory,an expected value model is presented to describe a stochastic job shop scheduling problem，in which the processing times are independent random variables following normal distribution.Initial computational results indicate the CCGSA performs effectively while comparing with basic GA.

关键词： Coevolutionary genetic algorithm job shop stochastic.

作者: Jin-wei Gu Xing-sheng Gu

作者单位: Department of AutomationEast China University of Science and Technology,Shanghai,China,200237 Department of Automation East China University of Science and Technology,Shanghai,China,200237

会议类型: 国际会议

会议名称: International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)

会议地点: 上海

会议语种:英文

在线出版日期: 2008-06-29（万方平台首次上网日期，不代表论文的发表时间）