随机型网络瓶颈容量扩张的机会约束模型与智能算法

A Chance-constrained Model and Intelligent Algorithm for Stochastic Network Bottleneck Capacity Expansion

文章研究的问题为,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,以至于网络瓶颈扩张的费用最小。本文假定每一条边的单位扩张费用wi是一个随机变量,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。随后,本文将网络瓶颈容量算法、随机模拟方法、神经网络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 decrease to maximum extent while the the final expansion capacity of a given family F of subsets of E is with a given limit bound. We suppose the cost W is a stochastic variable according to norm distribution. Network bottleneck capacity expansion problem with stochastic cost is originally formulated as Chance-constrained programming model according to some criteria. For solving the stochastic model efficiently, network bottleneck capacity algorithm, stochastic simulation, neural network and genetic algorithm are integrated to produce a hybrid intelligent algorithm. (Finally,) some numerical example are presented.