Journal
IEEE ACCESS
Volume 6, Issue -, Pages 79174-79199Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2884895
Keywords
Consensus ranking method; mixed choice strategy; multiple criteria decision analysis; Pythagorean fuzzy set; category-based strategy; criterion-specific strategy
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Funding
- Taiwan Ministry of Science and Technology [MOST 105-2410-H-182-007-MY3]
- Chang Gung Memorial Hospital [BMRP 574, CMRPD2F0203]
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The aim of this paper is to develop a novel consensus ranking method that uses a mixed choice strategy for multiple criteria decision analysis (MCDA) under complex uncertainty based on Pythagorean fuzzy (PF) sets. The majority of MCDA methods have focused almost exclusively on criterion-specific choice tasks that are the tasks in which all alternatives are decomposed into distinct components and evaluated on specific criteria. However, in certain MCDA problems in practical applications, category-based choices tend to be more holistic in nature, especially in affective-like aspects. Therefore, this paper incorporates a mixed choice strategy (i.e., a combination of a category-based strategy and a criterion-specific strategy) into the core structure of the developed MCDA method. Furthermore, this paper utilizes the theory of Pythagorean fuzziness to provide a powerful modeling tool for complex and varied decision-making environments. Employing the developed concepts of a PF precedence index based on PF information and a disagreement indicator based on distances between rankings, this paper proposes a novel consensus ranking method by means of a comprehensive disagreement-based assignment model for addressing a mixed-choice-strategybased MCDA problem in the PF context. As an application of the proposed methodology, a real-world case study of a luxury car selection problem is investigated. The application results, along with a comparative analysis, demonstrate the practicality and effectiveness of the developed approach, which is capable of handling hybrid category-based and criterion-specific choice tasks and managing complex uncertainty in practical situations.
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