An intuitionsitic fuzzy judgement matrix and TOPSIS integrated multi-criteria decision making method for green supplier selection

2015 ◽  
Vol 28 (1) ◽  
pp. 117-126 ◽  
Author(s):  
Qingwei Cao ◽  
Jian Wu ◽  
Changyong Liang
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.


2021 ◽  
pp. 1-11
Author(s):  
Huiyuan Zhang ◽  
Guiwu Wei ◽  
Xudong Chen

The green supplier selection is one of the popular multiple attribute group decision making (MAGDM) problems. The spherical fuzzy sets (SFSs) can fully express the complexity and fuzziness of evaluation information for green supplier selection. Furthermore, the classic MABAC (multi-attributive border approximation area comparison) method based on the cumulative prospect theory (CPT-MABAC) is designed, which is an optional method in reflecting the psychological perceptions of decision makers (DMs). Therefore, in this article, we propose a spherical fuzzy CPT-MABAC (SF-CPT-MABAC) method for MAGDM issues. Meanwhile, considering the different preferences of DMs to attribute sets, we obtain the objective weights of attributes through entropy method. Focusing on the current popular problems, this paper applies the proposed method for green supplier selection and proves for green supplier selection based on SF-CPT-MABAC method. Finally, by comparing existing methods, the effectiveness of the proposed method is certified.


Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 252 ◽  
Author(s):  
Chia-Nan Wang ◽  
Ying-Fang Huang ◽  
I-Fang Cheng ◽  
Van Nguyen

Suppliers are extremely important in business operations. The supplier ensures the supply of materials, raw materials, commodities, etc. in sufficient quantity, quality, stability, and accuracy to meet the requirements of production and business with low costs and on-time deliveries. Therefore, selecting and managing good suppliers is a prerequisite for organizing the production of quality products as desired, according to the schedule, and with reasonable prices and competitiveness in the market. It is also important to gain the support of suppliers in order to continue to improve and achieve more as a business. The evaluation and selection of a supplier is a Multi-Criteria Decision-Making (MCDM) issue, in which the decision-maker is faced with both qualitative and quantitative factors. In this research, the authors propose an MCDM model using a hybrid of Supply Chain Operations Reference metrics (SCOR metrics), the Analytic Hierarchy Process (AHP) model, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach for supplier evaluation and selection in the gas and oil industry. Using literature reviews on SCOR metrics, all criteria that impact supplier selection are defined in the first stage, the AHP model is applied to determine the weight of each factor in the second stage, and the optimal supplier is presented in final stage using the TOPSIS model. As a result, Decision-Making Unit 5 (DMU-05) is found to be the best supplier for the gas and oil industry in this research. The contribution of this work is to propose a new hybrid MCDM model for supplier selection in the gas and oil industry. This research also introduces a useful tool for supplier selection in other industries.


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