文献类型：会议论文

英文题名：An Improved Quantum Genetic Algorithm for Stochastic Job Shop Problem

作者：Gu, Jinwei[1];Cao, Cuiwen[1];Jiao, Bin;Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Shanghai 200237, Peoples R China

会议论文集：World Summit on Genetic and Evolutionary Computation (GEC 09)

会议日期：JUN 12-14, 2009

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Quantum algorithm; job shop; stochastic scheduling

摘要：This paper considers the stochastic job shop scheduling problem with the objective of minimizing the expected value of makespan and the processing times of jobs being subject to independent normal distributions. In order to solve this problem, we devise an Improved Quantum Genetic Algorithm (IQGA) and develop a stochastic expected value model. Different from traditional genetic algorithms, IQGA employs the idea of quantum theory, devises a converting mechanism of quantum representation aiming at job shop code, and proposes a new rotation angle table as the update mechanism of populatio. In addition, three crossover operators and three mutation operators are compared in order to obtain the best combination to improve algorithm performance. Compared with standard Genetic Algorithm (GA), experimental results achieved by IQGA demonstrate its feasibility and effectiveness while dealing with the stochastic job shop problem.