随机网络瓶颈容量扩张相关机会规划模型

Dependent-Chance Programming Model for Stochastic Network Bottleneck Capacity Expansion

文章研究的问题为,在不确定环境中,怎样去增加网络中一组边的容量到一个指定的容量,以至于网络瓶颈扩张的费用不超过给定的总费用上限的概率尽可能的大。本文假定每一条边的单位扩张费用Wi是一个随机的变量,它服从一定的概率分布。带有随机单位扩张费用W的网络瓶颈容量扩张问题可以根据一些规则,列出它的相关机会规划模型的通用表达式。随后,本文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,给出数值算例。

This paper considers how to increase the capacities of the elements in a set E efficiently so that probability of the total cost for the increment of capacity can be under an upper limit to maximum extent, while the final expansion capacity of a given family F of subsets of E has a given limit bound.The paper supposes the cost is a stochastic variable with some distribution.Network bottleneck capacity expansion problem with stochastic cost is originally formulated as dependent-chance programming model according to some criteria.For solving the stochastic model efficiently,network bottleneck capacity algorithm,stochastic simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm.Finally a numerical example is presented.