A BCF–CRITIC–WASPAS method for green supplier selection with cross-entropy and Archimedean aggregation operators

Лю, Пэйдэ; Saha, Abhijit; Mishra, Arunodaya Raj; Rani, Pratibha; Dutta, Dipak Kumar; Baidya, Jhulaneswar

doi:10.1007/s12652-022-03745-9

Cited by 20 publications

(8 citation statements)

References 92 publications

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“…Chen et al ( 2023) applied a method in a case study related to the production of environmentally friendly materials. Liu et al (2023) applied the WASPAS method to address the issue of selecting green suppliers. Singh (2024) optimized a solar water heating system to increase its efficiency.…”

Section: Overview Of Studies Related To Swara and Waspas Methodsmentioning

confidence: 99%

A Decision-Making Model for Prioritizing Low-Carbon Policies in Climate Change Mitigation

Qiu,

Bouraima,

Badi

et al. 2024

CIS

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Climate change (CC) represents a paramount environmental challenge, necessitating the deployment of sustainable, low-carbon strategies particularly in developing regions such as Africa. This study introduces a novel decision-making framework aimed at enhancing the prioritization of policies to combat the adverse effects of CC. The proposed two-stage model employs the integration of Step-Wise Weight Assessment Ratio Analysis (SWARA) and Weighted Aggregated Sum Product Assessment (WASPAS) under spherical fuzzy (SF) conditions to address the strategic sequencing of sustainable policies. Initially, SF-SWARA is utilized to ascertain the relative significance of diverse criteria. Subsequently, the SF-WASPAS method ranks these policies, facilitating informed decision-making. The primary obstacles identified include limited institutional capacity, insufficient financial resources, and technological constraints, for which strategic alternatives are proposed. Moreover, rigorous sensitivity and comparative analyses affirm the model's applicability. By systematically delineating and prioritizing necessary policies, this study contributes significantly to the scholarly discourse on climate mitigation (CM) in an African context.

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Section: Overview Of Studies Related To Swara and Waspas Methodsmentioning

confidence: 99%

A Decision-Making Model for Prioritizing Low-Carbon Policies in Climate Change Mitigation

Qiu,

Bouraima,

Badi

et al. 2024

CIS

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“… Farid & Riaz (2022) , Riaz et al (2020) proposed some AOs with applications to green supplier selection and Liu & Wang (2018) introduced interval-valued intuitionistic fuzzy Schweizer–Sklar power AOs with supplier selection applications. Liu et al (2022) used the techniques of the operational science for green supplier selection with cross-entropy and Archimedean AOs. Zulqarnain et al (2021) introduced some AOs for Pythagorean fuzzy soft sets with their application to green supplier chain management.…”

Section: Literature Reviewmentioning

confidence: 99%

A new approach to sustainable logistic processes with q-rung orthopair fuzzy soft information aggregation

Riaz,

Farid,

Razzaq

et al. 2023

PeerJ Computer Science

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In recent years, as corporate consciousness of environmental preservation and sustainable growth has increased, the importance of sustainability marketing in the logistic process has grown. Both academics and business have increased their focus on sustainable logistics procedures. As the body of literature expands, expanding the field’s knowledge requires establishing new avenues by analyzing past research critically and identifying future prospects. The concept of “q-rung orthopair fuzzy soft set” (q-ROFSS) is a new hybrid model of a q-rung orthopair fuzzy set (q-ROFS) and soft set (SS). A q-ROFSS is a novel approach to address uncertain information in terms of generalized membership grades in a broader space. The basic alluring characteristic of q-ROFS is that they provide a broader space for membership and non-membership grades whereas SS is a robust approach to address uncertain information. These models play a vital role in various fields such as decision analysis, information analysis, computational intelligence, and artificial intelligence. The main objective of this article is to construct new aggregation operators (AOs) named “q-rung orthopair fuzzy soft prioritized weighted averaging” (q-ROFSPWA) operator and “q-rung orthopair fuzzy soft prioritized weighted geometric” (q-ROFSPWG) operator for the fusion of a group of q-rung orthopair fuzzy soft numbers and to tackle complexities and difficulties in existing operators. These AOs provide more effective information fusion tools for uncertain multi-attribute decision-making problems. Additionally, it was shown that the proposed AOs have a higher power of discriminating and are less sensitive to noise when it comes to evaluating the performances of sustainable logistic providers.

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“…Fallahpour et al [35] assessed the reliability of the method with two techniques: the consistency ratio (CR) and various defuzzification techniques such as bisector of area (BOA) and smallest of maximum (SOM). In order to establish the advantage of the new model, Liu et al [36] conducted a comprehensive comparative analysis. Ecer [37] validated the reliability of the proposed model for green supplier selection with Spearman's rank correlation coefficient and sensitivity analysis on the findings.…”

Section: Approaches To Model Validationmentioning

confidence: 99%

A New Fuzzy Stochastic Integrated Model for Evaluation and Selection of Suppliers

Ulutaş,

Kiridena,

Shukla

et al. 2023

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In light of the rapid rate of change and unforeseen occurrences seen in the realms of technology, market dynamics, and the wider business landscape, there is a growing need for the inclusion of uncertainty and risk factors in the realm of supply chain planning. Supplier evaluation and selection (SES) is a major strategic decision area where the impact of uncertainty and risk can be more proactively dealt with. A review of extant literature reveals that there is a strong need for developing practitioner-oriented and more comprehensive frameworks and models to mitigate both the capability- and performance-related risks, in the context of SES decisions. This paper presents an integrated model to support SES decisions involving quantity discounts and multiple planning periods under stochastic conditions. The proposed model employs the Fuzzy Analytical Hierarchy Process (FAHP), Fuzzy Evaluation Based on Distance from Average Solution EDAS (EDAS-F), and fuzzy stochastic goal programming (FSGP) to effectively address the above requirements. A case study from a garment manufacturing industry is used to demonstrate the efficacy of the proposed model. The findings of the study provide confirmation that the suggested FSIM has the ability to provide substantial advantages in the context of making choices related to quantity discounts in SES. The proposed FSIM model incorporates the use of FAHP and EDAS-F techniques to effectively reduce the number of suppliers to a manageable level, taking into consideration capability-based risks. Additionally, fuzzy stochastic goal programming (FSGP) is employed to mitigate performance-based risks, enabling the selection of suppliers and the allocation of orders among them. The paper contributes to the literature by proposing a comprehensive framework to solve the SES problem, considering certain practical situations faced by organizations.

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A BCF–CRITIC–WASPAS method for green supplier selection with cross-entropy and Archimedean aggregation operators

Cited by 20 publications

References 92 publications

A Decision-Making Model for Prioritizing Low-Carbon Policies in Climate Change Mitigation

A Decision-Making Model for Prioritizing Low-Carbon Policies in Climate Change Mitigation

A new approach to sustainable logistic processes with q-rung orthopair fuzzy soft information aggregation

A New Fuzzy Stochastic Integrated Model for Evaluation and Selection of Suppliers

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