A robust TOPSIS method for decision making problems with hierarchical and non-monotonic criteria

Salvatore Corrente, Menelaos Tasiou

Research output: Contribution to journalArticlepeer-review

Abstract

This paper introduces an extension of a well-known Multiple Criteria Decision
Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions imply a normalization step, issues in which have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE’s FTSE350 Banks Index.
Original languageEnglish
JournalExpert Systems with Applications
Publication statusAccepted for publication - 12 Oct 2022

Keywords

  • TOPSIS
  • Non-monotonic criteria
  • qualitative criteria
  • Normalization
  • Hierarchy of criteria
  • Robustness concerns

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