Determining k-most demanding products with maximum expected number of total customers

Chen Yi Lin, Jia-Ling Koh, Arbee L.P. Chen

研究成果: 雜誌貢獻文章

22 引文 斯高帕斯(Scopus)

摘要

In this paper, a problem of production plans, named k-most demanding products (k-MDP) discovering, is formulated. Given a set of customers demanding a certain type of products with multiple attributes, a set of existing products of the type, a set of candidate products that can be offered by a company, and a positive integer k, we want to help the company to select k products from the candidate products such that the expected number of the total customers for the k products is maximized. We show the problem is NP-hard when the number of attributes for a product is 3 or more. One greedy algorithm is proposed to find approximate solution for the problem. We also attempt to find the optimal solution of the problem by estimating the upper bound of the expected number of the total customers for a set of k candidate products for reducing the search space of the optimal solution. An exact algorithm is then provided to find the optimal solution of the problem by using this pruning strategy. The experiment results demonstrate that both the efficiency and memory requirement of the exact algorithm are comparable to those for the greedy algorithm, and the greedy algorithm is well scalable with respect to k.

原文英語
文章編號6165292
頁(從 - 到)1732-1747
頁數16
期刊IEEE Transactions on Knowledge and Data Engineering
25
發行號8
DOIs
出版狀態已發佈 - 2013 八月 1

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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