Improve GMRACCF Qualifications via Collaborative Filtering in Vehicle Sales Chain

Abstract

The Vehicle Allocation Problem (VAP) in the vehicle sales chain has three bottlenecks in practice. The first is to collect relevant cooperation or conflict information, the second is to accurately quantify and analyze other factors affecting the distribution of cars, and the third is to establish a stable and rapid response to the vehicle allocation management method. In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain, it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy. Therefore, this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors (GMRACCF) from a new perspective. Through the logical reasoning of closure computation, the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors (CCF). Therefore, a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales (CSs) on the all-round performance according to their needs. Based on collaborative filtering, semantic modification is applied to resolve conflicts among qualifications. Large-scale simulation results show that the proposed method is feasible and robust, and provides a reliable decision-making reference in the vehicle sales chain.