一类网络扩张的机会约束模型与算法  

Chance-constrained programming model for network expansion

作　　者：吴云[1] 林毅[2] 周建 

机构地区：[1]武汉理工大学管理学院,武汉430073 [2]武汉大学化学与分子科学学院,武汉430072 [3]里昂大学计算机系

出　　处：《管理科学学报》2007年第2期7-11,共5页Journal of Management Sciences in China

基　　金：国家自然科学基金资助项目(70071011)

摘　　要：在不确定环境中的机会约束下,怎样去增加一组边的容量到一个指定的瓶颈容量,而使网络瓶颈扩张的费用最小.带有随机单位扩张费用的网络瓶颈容量扩张问题,可以根据一些概率机会约束规则,列出它的机会约束规划模型的通用表达式.将网络瓶颈容量算法、随机模拟方法和遗传算法合成在一起,设计出该问题的混合智能通用算法.最后,给出数值案例.In this paper we consider how to increase the capacities of the elements in a set E efficiently so that the total cost for the increment of capacity can be decreased to the maximum extent while the final expansion capacity of a given family F of subsets of E is within a given limit bound. We suppose that cost is a stochastic variable which conforms to normal distribution. Network bottleneck capacity expansion problem with stochastic cost is originally formulated as Chance-constrained programming model according to some criteria. In order to solve the stochastic model efficiently, network bottleneck capacity algorithm, stochastic simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical example are presented.

关 键 词：瓶颈容量扩张 机会约束规划模型 混合智能算法 

分 类 号：U121[交通运输工程]