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Sustainable Supplier Performance Evaluation and Selection with Neofuzzy TOPSIS Method
Author(s) -
S. Kamal Chaharsooghi,
Mehdi Ashrafi
Publication year - 2014
Publication title -
international scholarly research notices
Language(s) - English
Resource type - Journals
ISSN - 2356-7872
DOI - 10.1155/2014/434168
Subject(s) - topsis , computer science , selection (genetic algorithm) , operations research , artificial intelligence , mathematics
Supplier selection plays an important role in the supply chain management and traditional criteria such as price, quality, and flexibility are considered for supplier performance evaluation in researches. In recent years sustainability has received more attention in the supply chain management literature with triple bottom line (TBL) describing the sustainability in supply chain management with social, environmental, and economic initiatives. This paper explores sustainability in supply chain management and examines the problem of identifying a new model for supplier selection based on extended model of TBL approach in supply chain by presenting fuzzy multicriteria method. Linguistic values of experts' subjective preferences are expressed with fuzzy numbers and Neofuzzy TOPSIS is proposed for finding the best solution of supplier selection problem. Numerical results show that the proposed model is efficient for integrating sustainability in supplier selection problem. The importance of using complimentary aspects of sustainability and Neofuzzy TOPSIS concept in sustainable supplier selection process is shown with sensitivity analysis.

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