A novel Multiple Attribute Group Decision Making methodology based on Intuitionistic Fuzzy TOPSIS

Ervural, Beyzanur Çayır; Öner, Sultan Ceren; Çoban, Veysel; Kahraman, Cengiz

doi:10.1109/fuzz-ieee.2015.7338119

Cited by 4 publications

(6 citation statements)

References 39 publications

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“…When building the structure of an MCGDM model, if we fail to tackle explicitly the interactions among DMs, the decision information obtained during the MCGDM process may be overestimated or underestimated and can easily lead to distorted and unconvincing decision outcomes. However, the literature on the construction of MCGDM models has not delved into the interactions among DMs and their effects on decision information [4], [7], [18], [23], [24], [26], [27], [50], [56], [70], [75], [76], [81]. Bridging this gap has provided the motivation to develop a novel MCGDM model in the current study.…”

Section: Decision-making Environments Faced By Enterprises and Organimentioning

confidence: 99%

“…Different models have been proposed in the literature to solve diverse MCGDM problems. With respect to a common MCGDM problem in which the criterion values are set in different forms by DMs or experts and the weight information on criteria and that on DMs are both unknown, the process of addressing the MCGDM problem mainly includes, but is not limited to, the following steps [24], [56]:…”

Section: Literature Review a Multi-criteria Group Decision Makinmentioning

confidence: 99%

“…Owing to the increasing complexity and uncertainty of the decision-making environment, DMs prefer to articulate evaluation information in the form of fuzzy sets. Since its creation by Zadeh [88], the fuzzy set and its extended forms (such as intuitionistic fuzzy sets [24], [33], [35], [36], [39], [40]; hesitant fuzzy sets [10], [53]; type-2 fuzzy sets [51]; neutrosophic sets; and Pythagorean fuzzy sets) have occupied a prominent place in the theoretical development and practical application of MCDM and MCGDM [52], [54]. In the proposed MCGDM model, the IFNs are utilized by DMs to characterize uncertain evaluation information owing to their strong application potential.…”

Section: ) the Articulation And Representation Of Individual Evaluatmentioning

confidence: 99%

“…These methods can be categorized into four main groups [50]: non-compensatory methods; value-based methods; analytic hierarchical process (AHP) methods; and outranking methods. Compared with the other categories of MCDM methods, value-based methods such as simple additive weighting (SAW) [50], Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [24], [28], [31], [35], and VIšekriterijumsko Kompromisno Rangiranje (VIKOR) [75], are the most popular methods in the academic and application fields. Furthermore, SAW, in which the overall performance evaluations of each alternative obtained by aggregation functions serve as the basis for ranking alternatives, is the simplest and most widely used MCDM method among value-based methods [50].…”

Section: ) the Ranking And Selection Of Alternativesmentioning

confidence: 99%

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Intuitionistic Fuzzy Multiple Criteria Group Decision Making: A Consolidated Model With Application to Emergency Plan Selection

et al. 2019

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With complexity and uncertainty having an increasing impact on the decision-making environment, much attention is being paid to the development and application of multiple criteria group decisionmaking (MCGDM) models owing to the potential for fully exploiting the diverse strengths and expertise of various members. In general, inevitable interactions among decision makers (DMs), when a number of DMs share similar knowledge and experiences, can have a significant impact on the management of decision information directly or indirectly related to DMs, and can easily lead to distorted and unconvincing decision outcomes. In order to model the MCGDM problem in which DMs share a similar background, a consolidated MCGDM model in the context of intuitionistic fuzzy sets (IFSs) is developed. First, we refine the constructive principles for intuitionistic fuzzy entropy (IFE) and use them as a basis to produce a novel IFE measure simultaneously factoring in the intuitionism and fuzziness of IFSs. With the aim of dealing with the impact on the specifications of the weights of DMs and criteria, an integrated method is then proposed based on the novel IFE measure, 2-additive fuzzy measure, and Choquet integral. Due to their capability of modeling effectively the interrelationships among arguments, the weighted intuitionistic fuzzy Bonferroni mean (WIFBM) and the weighted intuitionistic fuzzy geometric Bonferroni mean (WIFGBM) are introduced to fuse the individual evaluation values of alternatives on criteria. In addition, simple additive weighting based on the WIFBM or WIFGBM is applied to rank alternatives and select the best one. Finally, the feasibility and effectiveness of the proposed model are explored with a case study of an emergency plan decision-making problem accompanied with sensitivity and comparison analysis.INDEX TERMS Intuitionistic fuzzy sets, Bonferroni mean, intuitionistic fuzzy entropy, 2-additive fuzzy measure, choquet integral, multiple criteria group decision making.

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Section: Decision-making Environments Faced By Enterprises and Organimentioning

confidence: 99%

Section: Literature Review a Multi-criteria Group Decision Makinmentioning

confidence: 99%

Section: ) the Articulation And Representation Of Individual Evaluatmentioning

confidence: 99%

Section: ) the Ranking And Selection Of Alternativesmentioning

confidence: 99%

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Intuitionistic Fuzzy Multiple Criteria Group Decision Making: A Consolidated Model With Application to Emergency Plan Selection

et al. 2019

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“…In group decision making problems, some researchers utilized various fuzzy number such as intuitionistic fuzzy number or hesitant fuzzy number to evaluate the alternatives [9][10][11]. However, experts may prefer subjective words or sentences to numbers as more intuitive and convenient to assess the options or alternatives.…”

Section: Introductionmentioning

confidence: 99%

Attitude quantifier based possibility distribution generation method for hesitant fuzzy linguistic group decision making

Hao

Chiclana

2020

Information Sciences

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The possibility distribution-based approach is one of the powerful tools available to manage hesitant fuzzy linguistic term set (HFLTS) information. However, existing possibility distribution studies have not considered the experts' satisfied preference for HFLTSs in the process of generating the possibility distribution. This paper aims at filling this research gap. To achieve this goal, a novel possibility distribution generation method based on the concept of linguistic quantifier is proposed. This is accomplished by defining a new attitude linguistic quantifier, which is supported with theoretical results to analyze the relationship between the proposed attitude linguistic quantifier with the original linguistic quantifier, attitude indices and the expected linguistic term. The new possibility distribution generation method is proved to be (1) more general than the two main existing approaches, which are particular cases for specific linguistic quantifiers; and (2) useful to implement the concept of soft majority in the resolution process of the decision making situation. Additionally, a new two stages feedback mechanism of attitude adjustment and assessment adjustment is devised to guarantee the convergence of the consensus reaching process. Finally, a framework of group decision making with HFLTSs information is presented and an illustrative example is conducted to verify the proposed method.

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Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

et al. 2021

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The aim of this research is to propose a hybrid decision-making model for evaluation and selection of quality methods whose application leads to improved reliability of manufacturing in the process industry. Evaluation of failures and determination of their priorities are based on failure mode and effect analysis (FMEA), which is a widely used framework in practice combining with triangular intuitionistic fuzzy numbers (TIFNs). The all-existing uncertainties in the relative importance of the risk factors (RFs), their values, applicability of the quality methods, as well as implementation costs are described by pre-defined linguistic terms which are modeled by the TIFNs. The selection of quality methods is stated as the rubber knapsack problem which is decomposed into subproblems with a certain number of solution elements. The solution of this problem is found by using genetic algorithm (GA). The model is verified through the case study with the real-life data originating from a significant number of organizations from one region. It is shown that the proposed model is highly suitable as a decision-making tool for improving the manufacturing process reliability in small and medium enterprises (SMEs) of process industry.

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A novel Multiple Attribute Group Decision Making methodology based on Intuitionistic Fuzzy TOPSIS

Cited by 4 publications

References 39 publications

Intuitionistic Fuzzy Multiple Criteria Group Decision Making: A Consolidated Model With Application to Emergency Plan Selection

Intuitionistic Fuzzy Multiple Criteria Group Decision Making: A Consolidated Model With Application to Emergency Plan Selection

Attitude quantifier based possibility distribution generation method for hesitant fuzzy linguistic group decision making

Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

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