Abstract

Purpose With the improvement of economic level, car ownership is growing, and the number of scrapped automobiles is increasing. Therefore, evaluation research for auto parts remanufacturing is particularly important. The purpose of this paper is to construct the evaluation index system of auto parts remanufacturing and research the grey clustering theory. The grey fixed weight clustering evaluation is used to evaluate automobile engine remanufacturability. Design/methodology/approach According to the policies and regulations of China about remanufacturing, economic, technical, resources, energy and the environment, four indexes are selected to set up the evaluation standard of auto parts remanufacturing scheme. Grey fixed weight clustering method is used to evaluate remanufacturability of the auto parts. Firstly, number index and grey determine the whitenization weight function, then based on the clustering weight of each index, the clustering coefficient matrix is calculated. Finally, the class that certain object belongs to, according to the clustering coefficient matrix is determined. Findings Results show that constructed indexes of auto parts remanufacturing scheme can be used for effective evaluation. And the proposed fixed weight grey cluster model can aggregate all indexes information well. Therefore, the proposed indexes and model in this paper are effective and can be used for auto parts remanufacturing. Practical implications According to the requirements of the current situation in China, this paper puts forward a method based on grey clustering decision, to evaluate different auto parts remanufacturing schemes, for manufacturing enterprises to provide theoretical basis for remanufacturing production, in order to realize the reasonable configuration of resources. Originality/value This paper firstly establishes the evaluation index system of auto parts remanufacturing, the grey clustering theory is introduced into the evaluation of remanufacturing. The fixed-weight grey cluster model is proposed to aggregate indexes’ information.

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