Using a Mixed Choice Strategy to Develop Compromising Decision-Making Models for Multiple Criteria Decision Analysis Within the Interval-Valued Intuitionistic Fuzzy Environment

The aim of this research project is to develop useful compromising decision-making models for managing multiple criteria decision analysis (MCDA) problems within the environment of interval-valued Atanassov’s intuitionistic fuzzy (A-IVIF) sets based on a mixed choice strategy. Uncertain and imprecise assessment of information often occurs in practical decision-making situations. The theory of A-IVIF sets is valuable for addressing the uncertainty of multiple criteria evaluations and quantifying the ambiguous nature of subjective assessments in a convenient way. From this perspective, this project considers anchored dependency (i.e., anchor subjective judgments with points of reference) to establish the theoretical framework and compromising methodology for MCDA based on A-IVIF sets. The majority of MCDA methods have focused almost exclusively on “criterion-specific” choice tasks, tasks in which all alternatives are decomposed into distinct components and compared on specific criteria. Criterion-specific choice requires the knowledge of specific criteria at the time at which the choice is made and involves criterion-by-criterion comparisons across alternatives. However, in many MCDA choices in the real world, category-based choices tend to be more holistic in nature. Category-based strategies involve the use of general attitudes, summary impressions, intuitions, heuristics, or combinations of these forms. Namely, an alternative is not decomposed into distinct components, each of which is evaluated separately from the whole. In reality, decision makers are likely to use a combination of processes rather than a single integration strategy in many problem-solving situations. The range of decision tasks that are category-based processing or mixed rather than purely criterion-specific processing is broad. Although research on category-based processing and judgment is highly pertinent to the present concerns, its implications for category-based and mixed choice are not entirely straightforward. Therefore, this project incorporates a mixed choice strategy (i.e., a combination of category-based strategies and criterion-specific strategies) into the developed compromising decision-making models. 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 projection-based compromising model based on a mixed choice strategy in the A-IVIF context for MCDA, (ii) developing an inclusion-based programming model for multidimensional analysis of preference based on a mixed choice strategy in the A-IVIF context for MCDA, and (iii) developing a compromising model with inclusion comparison possibilities for order preference by similarity to ideal solutions based on a mixed choice strategy in the A-IVIF context for MCDA. The feasibility and effectiveness of the proposed methods are illustrated by computational experiments, and certain comparative analyses with other compromising approaches are implemented to validate the advantages of the proposed methodologies. Additionally, this study will conduct real-world applications in MCDA problems with limited (low) problem solving (i.e., high knowledge and low involvement), limited (moderate) problem solving (i.e., high knowledge and high involvement), and extensive (or very limited) problem solving (i.e., low knowledge and high involvement) to examine the applicability of the developed compromising decision-making models.

Project ID:PB10701-1502
External Project ID:MOST105-2410-H182-007-MY3