Industrial Chain Data Evaluation in Automobile Parts Procurement via Group Multirole Assignment

Abstract

In the production process of automobiles, parts procurement is invariably a crucial step. In order to find an optimal decision, it is a challenge to match parts to suppliers for the limited financial and material capabilities of every supplier. This paper formalized the problem by Group Multirole Assignment (GMRA). Meanwhile, the success of this assignment process depends on the choice of the agent evaluation method. It depends on the industrial chain data, which can acquire feature indexes of parts from previous purchase records. Furthermore, comprehensive evaluation of parts procurement bases on multiple factors. Thus, it is difficult to reflect different quantifications using the multifactorial parameter semantics. Therefore, we propose a new method of Fuzzy Hierarchy Comprehensive Evaluation (FHCE), using membership grades of the fuzzy theory to differentiate the parameter and the weight, which can use objective quantitative analysis to optimize procurement plan. After that, based on GMRA, decision makers are able to maximize the resource utilization ratio to determine optimized solutions when funds or part types are limited. Simulation experiments indicate that the proposed method is efficient and feasible, which is verified practicable.