Supplier Selection Method for Complex Product Based on Grey Group Clustering and Improved Criteria Importance

Abstract

AbstractSupplier selection is of great significance and role, which influence the quality of major product development, and economic security and life safety. However, there exists a variety of uncertain information as a result of evaluation experts’ strong subjective consciousness and complexity of decision environment in the process of supplier selection evaluation. To deal with these problems, by exploiting gray incidence analysis, cloud models and TOPSIS, we establish a multi-attribute decision-making supplier selection method for complex product based on gray group clustering and improved criteria importance through intercriteria correlation, and then a case verifies the validity and feasibility of the proposed method. The results show that (1) the proposed model can provide a better portrayal of the uncertainty of the evaluation process in terms of both the fuzziness of the semantic concept and the randomness of the affiliation degree, while taking into account the differences between the evaluated solution and the positive and negative ideal solutions. (2) The proposed model can fully voice the decision-maker’s attitude on the basis of available information, allowing the decision-making process to be better tailored to reality by taking into account the ambiguity and randomness of the evaluation process.