Dependent-Chance programming model for stochastic network bottleneck capacity expansion based on neural network and genetic algorithm

Authors:
Yun Wu;Jian Zhou;Jun Yang
Affiliations:
College of Management, Wuhan University of Technology, Wuhan, Hubei, China;Department of Computer Sciences, University of Angers, France;College of Management, Huazhong University of Science and Technology, Wuhan, Hubei, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

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.