基于智能算法的制造系统通用作业调度方法  

Universal Shop Scheduling Method for Manufacturing System with Evolution Algorithm

作　　者：胡燕海[1] 严隽琪[1] 马登哲[1] 叶飞帆[2] 

机构地区：[1]上海交通大学CIM研究所,上海200240 [2]宁波大学工学院,宁波315211

出　　处：《上海交通大学学报》2008年第10期1608-1612,共5页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金资助项目(50575137);浙江省自然科学基金资助项目(Y607470);宁波市自然科学基金资助项目(2008A610036)

摘　　要：通过生产实际情况分析,提出了制造系统通用作业调度问题(USP)概念,开发了混杂蚁群算法(HACO),对USP进行求解,并与采用遗传算法所得解进行了对比.算例研究采用75×20个标准算例,以工件的加工流程时间最小化为目标函数,分别运用运算代数和解集收敛度为结束条件.计算结果表明,在计算代数相同时,HACO算法更容易使解域集中;在得到同等收敛度时,HACO算法的计算时间更短.The concept of universal shop scheduling problem （USP） was proposed based on the analysis of a real production system. A hybrid ant colony optimization （HACO） was developed to be applied to the USP. The results were compared with those of genetic algorithm. The numerical experiments make use of several benchmark instances whose scale is up to 75× 20. Minimizing makespan is taken as the objective function. Both termination conditions of computation generation and solution convergence are tested for the computation. From the numerical experiments, it can be seen that when the computation generation is kept the same, HACO will make the solutions more convergent, and when the convergency is kept the same, HACO will consume less time.

关 键 词：通用作业调度问题 智能算法 遗传算法 蚁群算法 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程] F273[自动化与计算机技术—控制科学与工程]