具有资源约束的最小化完工时间方差排序问题 Alternative Title Resource-Constrainted Scheduling Problem of Minimizing Completion Time Variance 葛迪 Thesis Advisor 王海明 2018-03-01 Degree Grantor 兰州大学 Place of Conferral 兰州 Degree Name 硕士 Keyword 资源限制 位置分配 完工时间方差 处理机加工资格限制 Abstract 本文在工件加工时间是所得到资源的函数(pj =bj-ajuj )的前提下对目标函数是最小化完工时间方差(CTV )的排序问题进行了研究.对于问题 ,通过实验和数据分析得到较优的位置分配方式是使具有最小加工时间的工件尽可能位于排序的中间,较优的资源分配方式是优先分配资源给aj 较大的工件.当m为定值时, n越大,排序问题Pm|unres|CTV 与Pm|res|CTV  的目标函数值比值越靠近m ,并得到了三个相应的推论.针对 Mj给出了三种工件和机器的分派方式.对一台处理机和多台同速机的CTV 问题分别构造了复杂度为O(n2) 的ASMH算法和复杂度为 O(mlogm+n2)的MJ算法. Other Abstract Based on the processing time of a job is a function of the resources it requires(pj =bj-ajuj  ), the article studies the scheduling problem of minimizing completion time variance . To  , we obtain the better way of position distribution is to make the job with the smallest processing time in the middle position of scheduling as far as possible , the better method of resourrce allocation is allocate resource firstly to job with the largest aj through experiment and data analysis . Then , we give three matching types between jobs and machines for Mj  . When m  is a definite value , we obtain the larger the n , the objective function value ratio of Pm|unres|CTV and Pm|res|CTV is close to  and prove three corresponding corollaries . What’s more , this article propose the ASMH algorithm with complexity is O(n2) for single processor and the MJ algorithm with complexity is O(mlogm+n2)  for multiple identical processor . URL 查看原文 Language 中文 Document Type 学位论文 Identifier https://ir.lzu.edu.cn/handle/262010/224953 Collection 数学与统计学院 Recommended CitationGB/T 7714 葛迪. 具有资源约束的最小化完工时间方差排序问题[D]. 兰州. 兰州大学,2018.
 Files in This Item: There are no files associated with this item.
 Related Services Recommend this item Bookmark Usage statistics Export to Endnote Altmetrics Score Google Scholar Similar articles in Google Scholar [葛迪]'s Articles Baidu academic Similar articles in Baidu academic [葛迪]'s Articles Bing Scholar Similar articles in Bing Scholar [葛迪]'s Articles Terms of Use No data! Social Bookmark/Share
No comment.