摘要
为提高自动小车存取系统中轨道导引小车系统的出入库作业效率,提出了一种基于改进量子微粒群的优化方法。分析了轨道导引小车系统出入库作业任务队列特征,建立了数学模型。在此基础上利用量子微粒群算法进行优化调度,并在该算法中引入高斯变异算子,克服了其容易陷入局部最优的缺点。通过仿真实验表明了方法的可行性和有效性。
To improve the performance of Rail-Guided Vehicles System（RGVS） in Automatic Vehicle Storage and Retrieval Systems（AVS/RS）,an optimization method based on improved Quantum Particle Swarm Optimization（QPSO） was proposed.Firstly,sequencing characteristics of tasks in RGVS were analyzed,and a mathematical model was established.Then,a QPSO algorithm was proposed to solve the scheduling problem.Meanwhile,the Gaussian mutation operator was introduced into this algorithm to overcome its shortcoming of falling into local convergence.Finally,feasibility and effectiveness of the presented method was shown by experimental results.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2011年第2期321-328,共8页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(60975052)
厦门大学国家"211三期工程建设"资助项目(0630-E62000)~~
关键词
自动小车存取系统
轨道导引小车系统
任务队列
量子微粒群算法
数学模型
automatic vehicle storage and retrieval systems
rail-guided vehicles system
sequencing of tasks
quantum particle swarm optimization algorithm
mathematical models
