CPT-MABAC method for spherical fuzzy multiple attribute group decision making and its application to green supplier selection

Zhang, Huiyuan; Wei, Guiwu; Chen, Xudong

doi:10.3233/jifs-202954

Cited by 44 publications

(11 citation statements)

References 57 publications

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“…But in the actual decision, many complex information forms may appear in the evaluation process due to the uncertainty of DMs and the diversity of attributes, such as spherical linguistic FSs (Ashraf et al 2018 ), spherical soft FSs (Perveen et al 2019 ), T-spherical hesitant FSs (Al-Quran 2021 ) and interval-valued T-SFSs (Garg et al 2022 ). Therefore, in future studies, we will also focus on the further extension of the CPT–CoCoSo method in the aforementioned environment to develop more MAGDM models for dealing with various uncertain problems (Zhang et al 2021 ; Liu et al 2021 ; Wu et al 2014 ; Lu et al 2019 ; Wang et al 2019b ).…”

Section: Discussionmentioning

confidence: 99%

Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

Zhang

Wei

2023

Comp. Appl. Math.

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Location selection of electric vehicle charging stations (LSEVCS) is a complex multi-attribute group decision-making (MAGDM) problem involving multiple experts and multiple conflicting attributes. Spherical fuzzy sets (SFSs) can deeply excavate fuzziness and uncertainty in MAGDM. In this paper, we first propose some new spherical fuzzy distance measures based on Dice and Jaccard indexes to detect the differences between SFSs or inputs. Secondly, considering risk preferences of decision makers, we integrate cumulative prospect theory (CPT) and combined compromise solutions (CoCoSo) method to develop a spherical fuzzy CoCoSo based on CPT (SF-CPT–CoCoSo) model for settling MAGDM issues. At the same time, we extend the improved CRiteria Importance Through Intercriteria Correlation (CRITIC) method, called the distance correlation-based CRITIC (D-CRITIC) method, to reasonably obtain unknown attribute weights under SFSs. Finally, this paper applies the developed model for LSEVCS to verify its practicability. Moreover, sensitivity analysis and comparative discussion with existing methods further demonstrate the robustness and effectiveness of the SF-CPT–CoCoSo model.

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Section: Discussionmentioning

confidence: 99%

Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

Zhang

Wei

2023

Comp. Appl. Math.

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“…MABAC method is used in different studies in the literature. This method is used for location selection (Bozanic et al 2019), third-party logistic provider selection (Pamucar et al 2019), portfolio selection (Biswas et al 2019), assessment of programming language (Mishra et al 2020), green supplier selection (Wei et al 2020), selection of emergency alternatives (Liang et al 2020) (Zhang et al 2021) and site selection (Wu et al 2021).…”

Section: Mabac Methodsmentioning

confidence: 99%

Personnel Performance Assessment using Entropy based MABAC Method: An Application in the Food Sector

KALEM¹,

Akpınar²

2022

Equinox Journal of Economics Business and Political Studies

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Personnel selection is one of the decisions of strategic importance in terms of ensuring sustainability of companies. This decision is referred to as a multi-criteria decision-making problem in the literature in terms of the many criteria it contains and the determination of the candidate who meets these criteria at the most appropriate level. The use of these methods in order to decide on the most suitable candidate accelerates the nomination process for the enterprise, while at the same time preventing the loss of time that may occur. In this study, an evaluation was made among the personnel who applied for a food company. In the evaluation, some of the criteria determined by the company manager and found in the literature were taken into consideration. Entropy method was used for criterion weights in the solution of the problem. Afterwards, the weights obtained from this method were integrated with the MABAC method, and it was aimed to rank the most suitable candidates. Finally, the ranking results obtained were interpreted and the most suitable candidate was decided.

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“…Also, the fuzzy MABAC approach was implemented for solving decision-making problems encountered in various fields. The main focal points of these papers can be summarized as follows: evaluation of the efficiency of social media using (Sun et al, 2019;Bobar et al, 2020), Risk assessment of rockburst (Liang et al, 2019), Assessment of undergraduate teaching (Gong et al, 2019), strategy selection for health tourism (Büyüközkan et al, 2020), the selection of location for the construction of single-span bailey bridge (Božanić et al, 2019), green supplier selection (Zhang et al, 2021), Selection of fire position of mortar units (Jokić et al, 2021), Selection of commercially available electric vehicle (Biswas & Das, 2019). As a result, studies focusing on logistics villages from the perspective of logistics companies and operators' preferences are limited.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Logistics Villages in Turkey Using Hybrid Improved Fuzzy Swara (Imf Swara) and Fuzzy Mabac Techniques

Zolfani

Görçün

Küçükönder

2021

Technological and Economic Development of Economy

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Positioning in the right location for organizing logistics activities is a determinative factor in the aspect of costs, effectivity, productivity, and performance of these operations carried out by logistics firms. The proper logistics village selection is a crucial, complicated, and time-consuming process for decision-makers who have to make the right and optimal decision on this issue. Decision-makers need a methodological frame with a practical algorithm that can be implemented quickly to solve these decision-making problems. Within this scope, the current paper aims to present an evaluation tool, which provides more reasonable and reliable results for decision-makers to solve the logistics village selection problem that is very complicated and has uncertain conditions based on fuzzy approaches. In this study, we propose the Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA), a modified and extended version of the traditional fuzzy Step-Wise Weight Assessment Ratio Analysis (F-SWARA) to identify the criteria weights. Also, we suggest applying the fuzzy Multi-Attributive Border Approximation area Comparison (F-MABAC) technique to determine the preference ratings of the alternatives. This combination has many valuable contributions. For example, it proposes to use a more reliable and consistent evaluation scale based on fuzzy sets. Hence, decision-makers can perform more reliable and reasonable pairwise comparisons by considering this evaluation scale. Besides, it presents a multi-attribute evaluation system based on the identified criteria weights. From this perspective, the proposed model is implemented to evaluate eight different logistics village alternatives with respect to nine selection criteria. According to the analysis results, while A8 is the most appropriate option, C1 Gross National Product (GNP) is the most significant criterion. A comprehensive sensitivity analysis was performed to test the robustness and validation of the proposed model, and the results of the analysis approve the validity and applicability of the proposed model. As a result, the suggested integrated MCDM framework can be applied as a valuable and practical decision-making tool to develop new strategies and improve the logistics operations by decision-makers.

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CPT-MABAC method for spherical fuzzy multiple attribute group decision making and its application to green supplier selection

Cited by 44 publications

References 57 publications

Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

Personnel Performance Assessment using Entropy based MABAC Method: An Application in the Food Sector

Evaluating Logistics Villages in Turkey Using Hybrid Improved Fuzzy Swara (Imf Swara) and Fuzzy Mabac Techniques

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