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

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

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6165292
Pages (from-to)1732-1747
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number8
DOIs
Publication statusPublished - 2013 Aug 1

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Keywords

  • Algorithms for data and knowledge management
  • Decision support
  • Performance evaluation of algorithm and systems
  • Query processing

ASJC Scopus subject areas

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

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