Green supply chain management with linguistic preferences and incomplete information

Authors:
Ming-Lang Tseng
Affiliations:
Lung Hwa University of Science and Technology, Graduate School of Business and Management, No. 300, Sec. 1,Wanshou Rd., Guishan Shiang, Taoyuan county, Taiwan
Venue:
Applied Soft Computing
Year:
2011

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Abstract

As firms move toward environmental sustainability, management must extend managements efforts to improve environmental practices across the supply chain. The selection of a suitable green supplier according to green supply chain management criteria (GSCM) is essential for the sustainable development of manufacturing firms. The objective of this study was to select an optimal alternative in the presence of incomplete information and linguistic preferences using multiple GSCM criteria. The goal of GSCM is to reduce a firm's pollution and other environmental impacts. In the proposed method, the weights of GSCM criteria and alternatives are described using linguistic preferences that can be resolved with fuzzy set theory. Subsequently, the rank of each alternative was calculated from incomplete information by applying a grey degree. Moreover, a case study was used to resolve the proposed method, and the results and managerial implications of the analysis are discussed in detail.