摘要
设计了2种不同实现方式的粒子群算法解决车间作业调度问题,即基于粒子位置矢量更新的实现方式和基于遗传操作的实现方式,同时选择一些典型的Job-Shop调度问题作为算法的试验对象。试验结果表明上述两种不同实现方式的粒子群算法在求解小规模调度问题时都能得到较好的结果,在求解较大规模调度问题时基于遗传操作的粒子群算法可以得到更好的结果。这一方面说明了上述两种不同实现方式的粒子群算法在求解调度问题上的有效性,同时也表明基于遗传操作的粒子群算法在求解较大规模调度问题上具有更大的优势。
Two approaches of particle swarm optimization (PSO), namely, the updating approach based on particle position vector and the approach based on genetic operation, are proposed for job-shop scheduling problems (JSSP). The typical job-shop scheduling problems are taken as examples to verify the performances of the proposed approaches, and satisfactory results are achieved in solving small-scale scheduling problems, and the approach based on genetic operation also can get satisfactory results in solving larger-scale scheduling problems. It's demonstrated that the approaches are effective in solving job-shop scheduling problems, and the approach based on genetic operation is more effective than the updating approach based on particle position vector in solving larger-scale scheduling problems.
出处
《制造技术与机床》
CSCD
北大核心
2009年第6期115-119,共5页
Manufacturing Technology & Machine Tool
基金
国家高技术研究发展计划(863计划)资助项目(2007AA04Z111)
关键词
粒子群算法
作业调度
位置矢量
遗传操作
Particle Swarm Optimization Algorithm
Job-Shop Scheduling
Position Vector
Genetic Operation
