Representing a probabilistic linguistic term set with an interval type-2 fuzzy set and the application in green supplier selection

2021 ◽  
pp. 1-18
Author(s):  
Le Jiang ◽  
Hongbin Liu

The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility.

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 182 ◽  
Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul

Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today’s organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.


Author(s):  
Melih Yucesan ◽  
Suleyman Mete ◽  
Faruk Serin ◽  
Erkan Celik ◽  
Muhammet Gul

Supplier selection is one of the most important multi criteria decision making (MCDM) problems for decision makers in competitive market. Organizations of today’s world are seeking new ways to reduce negative effects of their organizations to the environment and to reach a greener system. At this point, green supplier selection concept has gained great importance with its ability on incorporating environmental or green criteria into the classical supplier selection practices. Therefore, in this study, it is aimed at proposing a multi-phase MCDM model based on Best-Worst Method (BWM) and interval type-2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS). A case study in a plastic injection molding facility in Turkey is performed to show the applicability of the proposed integrated methodology. The paper ensures insights into the decision making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.


Kybernetes ◽  
2019 ◽  
Vol 49 (4) ◽  
pp. 1229-1252 ◽  
Author(s):  
Morteza Yazdani ◽  
Prasenjit Chatterjee ◽  
Dragan Pamucar ◽  
Manuel Doval Abad

Purpose Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk. Design/methodology/approach At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics. Findings A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model. Practical implications The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors. Originality/value A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.


Author(s):  
Beyza Ahlatcioglu Ozkok ◽  
Hale Gonce Kocken

Analytic hierarchy process (AHP) is a widely used multi-attribute decision-making (MADM) approach. Due to the complexity and uncertainty involved in real world problems, decision makers might be prefer to make fuzzy judgments instead of crisp ones. Furthermore, even when people use the same words, individual judgments of events are invariably subjective, and the interpretations that they attach to the same words may differ. This is why fuzzy numbers has been introduced to characterize linguistic variables. Fuzzy AHP methods have recently been extended by using type-2 fuzzy sets. Type-2 fuzzy set theory incorporates the uncertainty of membership functions into the fuzzy set theory. In this chapter, the authors firstly provide a short review on applications of interval type-2 fuzzy AHP on MADM problems. Then, they present a very efficient MADM technique, interval type-2 fuzzy AHP, to solve the portfolio selection problem that is to decide which stocks are to be chosen for investment and in what proportions they will be bought. And finally, they provided a case study on BIST.


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