摘要
在不确定的环境下,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。假定每一条边的单位扩张费用Wi是一个随机的变量,它服从正态分布。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统计规则,列出它的期望值模型的通用表达式。随后,网络瓶颈容量算法、随机模拟方法和遗传算法将合成在一起,设计出该问题的混合智能算法。最后,给出数值案例。
In this paper, how to increase the capacities of the elements in a set E（Edge） efficiently is considered, so that the total cost for the increment of capacity can be decrease to maximum extent while the final expansion capacity of a given family F of subsets of E is with a given limit bound, Suppose the cost w is a stochastic variable according to norm distribution, the network bottleneck capacity expansion problem with stochastic cost is originally formulated as expected value model following 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.
出处
《工业工程与管理》
2005年第4期22-25,30,共5页
Industrial Engineering and Management
基金
国家自然科学基金资助项目(70071011)
关键词
瓶颈容量扩张
期望值模型
混合智能算法
随机规划
bottleneck capacity expansion
expected value model
hybrid intelligent algorithm
stochastic programming
