Quantum Genetic Algorithm for Hybrid Flow Shop Scheduling Problems to Minimize Total Completion Time

摘要：

This paper investigates the application of the quantum genetic algorithm (QGA) for Hybrid flow shop problems (HFSP) with the objective to minimize the total completion time. Since HFSP has shown to be NPhard in a strong sense when the objective is to minimize the makespan in case of two stages, an efficient QGA is proposed to solve the problem. A real number representation is used to convert the Qbit representation to job permutation for evaluating the solutions and quantum rotation gate is employed to update the population. Two different types of crossover and mutation operators are investigated to enhance the performance of QGA. The experimental results indicate that QGA is capable of producing better solutions in comparison with conventional genetic algorithm (GA) and quantum algorithm (QA).

关键词： Hybrid flow shop scheduling Genetic algorithm Quantum algorithm total completion time

作者: Qun Niu Fang Zhou Taijin Zhou

作者单位: Shanghai Key Laboratory of Power Station Automation Technology,School of Mechatronic Engineering and Automation,Shanghai University, 200072 Shanghai, China

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 21-29

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）