The Geomorphosite Assessment by Trapezoidal Bipolar Fuzzy Information-Based CRITIC-MABAC Method

Zorlu, Kuttusi; Dede, Volkan

doi:10.1007/s12371-023-00859-0

Cited by 7 publications

(2 citation statements)

References 53 publications

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“…(2021). Some researchers have improved the method by converting fuzzy numbers into precise numbers, as seen in the study by Zorlu and Dede (2023). Others have omitted the standardization process altogether, such as Mandal and Seikh (2023), who directly apply standardization through aggregation operators.…”

Section: Discussionmentioning

confidence: 99%

Supplier selection for carbon emission reduction collaboration in green supply chain using an improved multi-criteria decision-making method

Wang,

Zhang,

et al. 2024

APJML

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PurposePlatform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.Design/methodology/approachThis paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.FindingsIn this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.Originality/valueFirstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

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Section: Discussionmentioning

confidence: 99%

Supplier selection for carbon emission reduction collaboration in green supply chain using an improved multi-criteria decision-making method

Wang,

Zhang,

et al. 2024

APJML

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“…The modified MABAC method with Z -numbers was utilized by Božanic et al ( 2023 ) to identify the most effective contingency strategy for managing oil release risks. Zorlu and Dede ( 2023 ) used CRITIC method to analyze criteria weights, while MABAC established their priority values for alternatives.…”

Section: Introductionmentioning

confidence: 99%

Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making

Rasheed,

Mahboob,

Mustafa

et al. 2024

Front. Artif. Intell.

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IntroductionBreast cancer is an extremely common and potentially fatal illness that impacts millions of women worldwide. Multiple criteria and inclinations must be taken into account when selecting the optimal treatment option for each patient.MethodsThe selection of breast cancer treatments can be modeled as a multi-attribute group decision-making (MAGDM) problem, in which a group of experts evaluate and rank alternative treatments based on multiple attributes. MAGDM methods can aid in enhancing the quality and efficacy of breast cancer treatment selection decisions. For this purpose, we introduce the concept of a 2-tuple linguistic interval-valued q-rung orthopair fuzzy set (2TLIVq-ROFS), a new development in fuzzy set theory that incorporates the characteristics of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) and 2-tuple linguistic terms. It can express the quantitative and qualitative aspects of uncertain information, as well as the decision-makers' level of satisfaction and dissatisfaction.ResultsThen, the 2TLIVq-ROF weighted average (2TLIVq-ROFWA) operator and the 2TLIVq-ROF weighted geometric (2TLIVq-ROFWJ) operator are introduced as two new aggregation operators. In addition, the multi-attribute border approximation area comparison (MABAC) method is extended to solve the MAGDM problem with 2TLIVq-ROF information.DiscussionTo demonstrate the efficacy and applicability of the suggested model, a case study of selecting the optimal breast cancer treatment is presented. The results of the computations show that the suggested MAGDM model is able to handle imprecision and subjectivity in complicated decision-making scenarios and opens new research scenarios for scholars.

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Assessment of Resources for Geotourism Development: Integrated SWARA-COBRA Approach Under Spherical Fuzzy Environments

Zorlu,

Tuncer,

Yılmaz

2024

Geoheritage

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The Geomorphosite Assessment by Trapezoidal Bipolar Fuzzy Information-Based CRITIC-MABAC Method

Cited by 7 publications

References 53 publications

Supplier selection for carbon emission reduction collaboration in green supply chain using an improved multi-criteria decision-making method

Supplier selection for carbon emission reduction collaboration in green supply chain using an improved multi-criteria decision-making method

Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making

Assessment of Resources for Geotourism Development: Integrated SWARA-COBRA Approach Under Spherical Fuzzy Environments

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