Supplier selection in Telecom supply chain management: a Fuzzy-Rasch based COPRAS-G method

Chatterjee, Kajal; Kar, Samarjit

doi:10.3846/20294913.2017.1295289

Cited by 47 publications

(24 citation statements)

References 46 publications

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“…Zheng et al [50] proposed a hesitant fuzzy linguistic COPRAS method and applied the same for medical application. Chatterjee and Kar [5,51] presented a hybrid model by extending the COPRAS method fuzzy and Z-number context and used the same for the evaluation of the telecommunication industry and renewable energy sources.…”

Section: Q-rofs Based Copras Methodsmentioning

confidence: 99%

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

et al. 2019

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As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes' weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.Sustainability 2019, 11, 4202 2 of 21 inferred from their analysis that India has a high scope for renewable energy sources and it can effectively manage energy crisis by proper planning and management. Recently, Mardani et al. [4] conducted a detailed analysis on the use of multi-attribute group decision-making (MAGDM) methods for solving energy management problems, and it can be inferred from the analysis that energy source evaluation and selection can be effectively solved by using MAGDM perspectives. Furthermore, there is uncertainty in the process of selection, which can be effectively managed by using fuzzy sets and its variants [5]. Baek and Lee et al. [6] proposed a new design strategy for optimal selection of renewable energy system (RES) in buildings. Gonzalez et al. [7] presented a conceptual model to understand the relationship among different factors that correspond to the sustainability and acceptance of RES projects. Cavallaro et al. [8,9] presented decision frameworks under intuitionistic fuzzy context to rationally select solar-hybrid power plants.Motivated by these claims, in this paper, we propose a new decision framework for rational prioritization of renewable energy sources. The preference information used here is q-rung orthopair fuzzy set (q-ROFS) [10], which is a powerful generalization of the intuitionistic fuzzy set (IFS) [11] and Pythagorean fuzzy set [12]. q-ROFS provides a wider window to decision makers (DMs) for preference elicitation by relaxing the constraint (sum of the degree of membership and non-membership less than or equal to...

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Section: Q-rofs Based Copras Methodsmentioning

confidence: 99%

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

et al. 2019

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“…Recently, attention paid to the ARAS method, a relatively novel tool for MCDM (Turskis & Zavadskas, 2010a, 2010bZavadskas & Turskis, 2010), according to the theory that states that the accurate understanding of the world complex phenomena is possible through simple relative comparisons (Buyukozkan & Gocer, 2018;Heidary Dahooie, Beheshti Jazan Abadi, Vanaki, & Firoozfar, 2018;Liao, Fu, & Wu, 2015;Tamosaitiene, Zavadskas, Sileikaite, & Turskis, 2017) The ARAS method adopts the optimality degree concept to find the ranking. It is equal to the sum of weighted normalised values of the criteria with respect to each of the alternatives divided by the sum of weighted normalised values of the best option (Chatterjee & Kar, 2018;Ecer, 2018;Heidary Dahooie, Zavadskas, Abolhasani, Vanaki, & Turskis, 2018;Rostamzadeh, Esmaeili, Nia, Saparauskas, & Keshavarz Ghorabaee, 2017;Sivilevicius, Daniunas, Zavadskas, Turskis, & Susinskas, 2012;Turskis, Kersuliene, & Vinogradova, 2017;Turskis, Morkunaite, & Kutut, 2017) Step 1: First, an m Â n decision matrix is formed, where m represents the alternatives and n represents the criteria.…”

Section: Additive Ratio Assessment (Aras) Methodsmentioning

confidence: 99%

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

Dahooie

Zavadskas

Vanaki

et al. 2019

Economic Research-Ekonomska Istraživanja

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The financial performance is an indicator of financial stability, health and condition of any organisation. It could be utilised as a proper measure of the firm's credibility and its ability to pay off debts. Financial institutions use this measure to determine the lending policy and applicants' credits. This study proposes a model based on the CCSD weighing method and hybrid FCM-ARAS approach for clustering and evaluating the financial performance to enable banks to identify target groups and design appropriate and relevant policies. Based on previous studies and the views of senior financial managers of a public bank in Iran, eight economic criteria were evaluated. The presented method was used to assess the financial performance of 58 manufacturing companies applying for loans from a federal bank in Iran. However, the CCSD method was used to calculate criteria weights, and a hybrid FCM-ARAS approach was developed and applied to financial evaluation and clustering the companies. The use of the CCSD method can eliminate errors caused by subjective models and human judgments, and increase the accuracy of the assessment. In this study, the debt ratio and equity to total assets and ROA were identified as the main criteria to assess financial performance.

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“…Recently, many different MCDM methods are used to solve the supplier selection problem. For example, the best worst method (Rezaei et al, 2016), linguistic MCDM method (Cid-Lopez et al, 2016), fuzzy EDAS , TOPSIS-MMD (Aouadni et al, 2017), intuitionistic VIKOR (Zhao et al, 2017), fuzzy Rasch based COPRAS-G (Chatterjee & Kar, 2018) and neutrosophic DEMATEL (Abdel-Basset et al, 2018). The green supplier selection problem takes into account also environmental factors, unlike the common supplier selection problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Supplier Selection by Using a Fuzzy Integrated Model for a Textile Company

Ulutaş

2019

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Supply chain management determining the competitive position of corporations in a rival and fast-changing environment is one of the favourite topics in engineering economics. Since the performance of suppliers affects the performance of the whole supply chain, the selection of a supplier is a significant problem in supply chain management. When solving a supplier selection problem, there are many criteria needed to be considered. Therefore, the supplier selection problem is a multi-criteria decision making (MCDM) problem. In the supplier selection process, most of the time, expert data is involved, and the judgement of experts includes ambiguous, imprecise and uncertain data. Thus, there are many methods to solve supplier selection issues by utilising fuzzy set theory, grey theory and rough theory to handle vagueness of a problem. This study also proposes a fuzzy model to solve this problem. In this study, an integrated fuzzy MCDM model including the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Operational Competitiveness RAting (Fuzzy OCRA) will be proposed to solve supplier selection problem for a Turkish textile company. The FAHP is utilised to determine the weights of considered eight criteria and the fuzzy OCRA is used to rank five fabric suppliers concerning their performances on eight measures. This study is original and it contributes to the literature in two ways: first, this study proposes a fuzzy extension of the OCRA method and second, this study offers a new integrated fuzzy MCDM model consisting of the FAHP and the Fuzzy OCRA. By using a real case study of supplier selection problem, it is attempted to indicate the applicability of the new integrated fuzzy MCDM model for actual circumstances.

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Supplier selection in Telecom supply chain management: a Fuzzy-Rasch based COPRAS-G method

Cited by 47 publications

References 46 publications

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

Scientific Decision Framework for Evaluation of Renewable Energy Sources under Q-Rung Orthopair Fuzzy Set with Partially Known Weight Information

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

Supplier Selection by Using a Fuzzy Integrated Model for a Textile Company

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