A novel summarization technique for the support of resolving multi-criteria decision making problems

Tony Cheng Kui Huang, Yen Liang Chen, Ting Hao Chang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In real-life circumstances, managers often have to face various decision-making problems. Among them, the topic of multi-criteria decision making (MCDM) is one of the most important and complicated problems in the decision-making field. In general, the element of MCDM can consist of three parts: input, output, and the solution approach. The input is expressed as m alternatives with n criteria. It usually assumes that the underlying input data can be represented in a decision table. Correspondingly, the output is an optimal outcome, or a set of outcomes, resolved by different kinds of solution approaches. In this study, we propose a summarization technique to display the decision table as a new summarization table; allowing managers to make decisions more quickly. The proposed technique is to optimize a summarization result so that the degree of information lost is minimum. Since seeking for a minimum result is an NP-hard problem, we applied a genetic algorithm to improve the summarization result.

Original languageEnglish
Article number12641
Pages (from-to)109-124
Number of pages16
JournalDecision Support Systems
Volume79
DOIs
StatePublished - 1 Nov 2015

Keywords

  • Decision table
  • Genetic algorithm
  • Multi-criteria decision making
  • Summarization

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