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A Type Of Group-buying Bid Model Based On Product Combination

Posted on:2012-12-07Degree:MasterType:Thesis
Country:ChinaCandidate:F XuFull Text:PDF
GTID:2249330392958088Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
In recent years, along with increased Internet penetration and e-commercedevelopment,group-buying has become a flourish shopping form on internet platform.The number of business experienced an explosive growth, and the object for group-buyingis various. With the development of2.0model, the new group-buying model which iscalled3.0model began to take shape, which can be found from the operation ofgroup-buying navigation site. Although the group-buying has developed to some extent,there’re no much studies. So it’s necessary to study the group-buying under newbackground, which is also valuable to practice.The paper takes the group-buying as research background, concentrated effort tosolve the decision making of group-buying with a variety of products. Firstly, the paperanalyzes related study domestic and abroad, clarifies the background and significance ofthis paper. On this basis, analyzes the phenomenon of networks buying systematically,detailed the development of a variety of typical group-buying modes, and summarizes thecharacteristics of3.0model. For the defects of a kind of current typical model, the paperestablished auction platform focused model with a variety of commodities, in order toachieve all the goals of maximizing consumer surplus of buyers. The model can get thebest configuration and goods transaction price under this configuration. As the complexityof brute-force method to solve the problem, the paper designs a heuristic algorithm andevaluates its performance by comparing experiment results. Through the analysis ofexperimental results, it shows that the algorithm is very close to optimal results with goodstability. This multi-product group-buying model achieves the desired results. Finally, aftersumming up the conclusions of the study, the paper proposes relevant future researchprospects.
Keywords/Search Tags:Group-buying, Product portfolio, Bidding, Algorithm
PDF Full Text Request
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