A novel order evaluation model with nested probabilistic-numerical linguistic information applied to traditional order grabbing mode

Abstract

With the popularization of information technology and the acceleration of the people’s pace of life, the takeout food industry is prevailing. The choice of order allocation mode plays an important role in order delivery efficiencies. This paper firstly reviews the whole process of the order grabbing mode and its internal logic, and then analyzes the qualitative and quantitative factors that influence the order distribution efficiency. Next, the order evaluation model is established based on the nested probabilistic-numerical linguistic information. After that, influencing factors of the order allocation modes are established, and the weights of the factors are determined by the AHP method. Finally, the order distribution results are obtained by traditional mode and the novel mode respectively. The comparative analysis and further analysis verify the validity and operability of the novel mode. By comparing the final values of multi-criteria functions between two modes, we conclude that the novel mode improves the allocation efficiency of order grabbing mode. In addition, the proposed mode significantly reduces the service distance and the standard deviation of service distance. The completion rate of delivery orders and the consistency of service level are also greatly improved. The takeout order allocation problem is optimized through the order evaluation model based on the nested probabilistic-numerical linguistic information. The proposed method has guiding effect on similar platforms.