Towards fuzzy preference relationship based on decision making approach to access the performance of suppliers in environmental conscious manufacturing domain. (March 2017)

• Record Type: Journal Article
• Authors: Sinha, Amit Kumar; Anand, Ankush
• Is Part Of: Computers & industrial engineering. Volume 105(2017)
• Page Start: 39
• Page End: 54
• Publication Date: 2017-03
• Subjects: Environmental Conscious Manufacturing (ECM) -- Environmental Impact (EI) -- Multi Attribute Decision Making (MADM) -- Decision support system -- Green supply chain -- Fuzzy systems; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.12.033
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Environmentally conscious supplier selection model is proposed. A fuzzy framework of multi attribute decision making process is proposed. Importance of environmental impact in supply chain management are studied. Abstract: Environmental consciousness has become a significant aspect at various levels of product design. In the recent years, by and large industries are focusing towards environment conscious products. Quantitative analysis of energy consumption and waste generation from any product is directly related to the supply chain management of the product. Hence, the selection of most suitable supplier from the pool of available suppliers is emerged as an important multi attribute (multi objective, multi criteria, multi factor) decision making (MADM) problem. This research paper addresses the supplier selection problem from the perspective of supply analysis, logistic analysis, process analysis, use analysis and, recycle analysis for calculating environmental impact of a product. Based on the associated environmental importance of a product a procedure based on fuzzy framework is used for ranking and selecting the best suitable supplier from its environmental perspective. A multi preference fuzzy relationship model is introduced to incorporate the uncertainty in the decision making by the decision maker. Finally, a solution procedure based on OWA (Ordered Weighted Averaging) aggregation operator is developed. The proposed methodology has been discussed along with an illustrative examples.