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 is with a given limit bound. The paper supposes the cost w is a stochastic variable according to 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, neural network(NN) and genetic algorithm(GA) are integrated to produce a hybrid intelligent algorithm. Finally a numerical example is presented. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Wu, Y., Zhou, J., & Yang, J. (2005). Dependent-chance programming model for stochastic network bottleneck capacity expansion based on neural network and genetic algorithm. In Lecture Notes in Computer Science (Vol. 3612, pp. 120–128). Springer Verlag. https://doi.org/10.1007/11539902_14
Mendeley helps you to discover research relevant for your work.