Multiple Criteria Decision Analysis Methods for Addressing Decision-Making Difficulty Problems under Complex Uncertainty Based on Pythagorean Fuzzy Sets

Project: National Science and Technology CouncilNational Science and Technology Council Academic Grants

Project Details


The purpose of this research project is to consider difficulties encountered before and during the decision-making process to construct novel multiple criteria decision analysis (MCDA) models for addressing decision-making difficulty problems in making multiple criteria evaluation, prioritization, and selection under uncertain and imprecise information with Pythagorean fuzziness. Decision-making difficulties that decision makers face when making multiple criteria decisions includes three general types of difficulties, which originate from a lack of readiness, lack of information, and inconsistent information. Lack of readiness includes diminished motivation to embark on the MCDA task, a general indecisiveness in making decisions, and dysfunctional myths about decision making. Decision makers may experience lack of information in the form of inadequate knowledge about the procedures involved in the decision-making process; lack of information about multiple, usually conflicting, criteria and their preferences; lack of information about available alternatives and their characteristics; and the ways through which one might obtain such information about criteria and alternatives. Difficulties arising from inconsistent information are based on unreliable information, a lack of compatibility between decision makers’ preferences and capabilities, and a lack of congruence between decision makers’ own preferences and that of significant stakeholder’ opinions. Nonetheless, there has been less investigation into how to incorporate these decision-making difficulties into the modelling process to manage MCDA problems under uncertain decision environments. Decision information is often vague and ambiguous because of a lack of data, time pressure, or the decision maker’s limited attention and information-processing capabilities. The theory of Pythagorean fuzzy (PF) sets possesses significant advantages in handling vagueness and complex uncertainty. Information based on Pythagorean fuzziness is useful to simulate the ambiguous nature of subjective judgments and measure the fuzziness and imprecision more flexibly. Moreover, as an extension of PF sets, interval-valued Pythagorean fuzzy (IVPF) sets have wider application potential because of their superior ability to manage more-complex uncertainty and address strong fuzziness, ambiguity, and inexactness in real-life situations. Based on the motivational issues of filling research vacancy from reviews of decision-making difficulties, incorporating decision-making difficulties into the modelling process of MCDA, and handling higher order uncertainties based on Pythagorean fuzziness, this project attempts to propose several effective methods that works with some novel concepts and approaches for managing MCDA problems with decision-making difficulties in the PF and IVPF contexts, which is evidently different from the existing methods and techniques. The research period for this project is three years, and the following main topics are addressed during each of the three years: (i) developing a PF preference ranking organization method for enrichment evaluations (PROMETHEE)-based method using a combinative distance-based precedence approach, (ii) developing an inferior ratio (IR)-based median ranking method in the PF and IVPF contexts, and (iii) developing a Pearson-like correlation-based technique for order preference by similarity to ideal solutions (TOPSIS) model for addressing decision-making difficulty problems in MCDA under complex uncertainty of Pythagorean fuzziness.

Project IDs

Project ID:PB10901-3459
External Project ID:MOST108-2410-H182-014-MY2
Effective start/end date01/08/2031/07/21


  • Multiple criteria decision analysis
  • decision-making difficulty
  • Pythagorean fuzziness
  • PF PROMETHEE-based method
  • IR-based median ranking method
  • Pearson-like correlation-based TOPSIS model.


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