Stochastic Programming Models and Hybrid Intelligent Algorithm for Unbalanced Bidding Problem

Xingzi Liu, Liang Lin, Dongran Zang

Abstract


The expected value model and the chance-constrained programming model for unbalanced bidding problem are established on the condition that quantities of each activity are stochastic variables and the total project is finished smoothly in this paper. These models can make the unbalanced bidding price more reasonable and applicable. In order to solve these models, stochastic simulation, neural network and genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, a numerical example is given to illustrate its effectiveness.


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Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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