JOB SHOP SCHEDULING WITH STOCHASTIC PROCESSING TIME THROUGH GENETIC ALGORITHM

摘要：

This paper deals with job shop scheduling with stochastic processing time in normal distribution. The extended Giffler-Thompson procedure in the stochastic context is first presented and some operations on the stochastic processing time are defined. A new permutation-based representation method is then proposed, in which the substring related to each machine is a permutation. The conflict among the competing operations is eliminated by giving priority to the operation with the minimum gene value in the same permutation. An efficient genetic algorithm is proposed to minimize the maximum completion time of jobs. The proposed algorithm is tested on a set of benchmark problems and compared when it is endowed with different crossover and mutation. The computational results demonstrate the effectiveness of the proposed genetic algorithm.

关键词： Genetic algorithm Stochastic processing time Job shop scheduling

作者: DE-MING LEI HE-JING XIONG

作者单位: School of Automation, Wuhan University of Technology, Wuhan, Hubei Province, PR China

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: 941-946

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）