Interactive Approaches to Multiple Criteria Sorting Problems: Entropy-Based Question Selection Methods

Ozarslan A., KARAKAYA G.

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, vol.22, no.01, pp.279-312, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 01
  • Publication Date: 2023
  • Doi Number: 10.1142/s0219622022500389
  • Journal Name: INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.279-312
  • Keywords: Multiple criteria sorting, additive utility function, linear programming, mixed integer programming, relative entropy, category size restriction, PREFERENCE DISAGGREGATION, ORDINAL REGRESSION, DECISION-MAKING, ELICITATION, SET, ALTERNATIVES, ALGORITHMS, RANKING
  • Middle East Technical University Affiliated: Yes

Abstract

In this study, interactive approaches for sorting alternatives evaluated on multiple criteria are developed. The possible category ranges of alternatives are defined by mathematical models iteratively under the assumption that the preferences of the decision maker (DM) are consistent with an additive utility function. Simulation-based and model-based parameter generation methods are proposed to hypothetically assign the alternatives to categories. A practical approach to solve the incompatibility problem of the randomly generated parameters is developed. Based on the hypothetical assignments, the assignment frequencies of alternatives for each possible category are defined. Then, an information theoretic measure, relative entropy, is used in the selection of the alternative that will be assigned into a category by the DM. The performance of our approaches is tested on different problems with/without initial assignments and category size restrictions. The results show that relative entropy-based alternative selection methods work well in decreasing the assessment burden of DM.