Research on delivery problem based on two-stage multi-objective optimization for takeout riders

Abstract

From the perspective of takeout riders, in order to ensure the safety of riders, reasonably arrange the distribution tasks of riders and improve the distribution efficiency of the platform, this paper proposes a new distribution mode. In the future, efficiency is the core competitiveness of the takeout platform, and humanization is an important condition for the long-term development of the takeout platform. Both are indispensable. This paper simplifies the traditional many to many distribution mode into one to many distribution mode, and realizes the new distribution mode through two-stage multi-objective optimization algorithm. In the first stage, genetic algorithm is applied to analyze and optimize the new distribution mode for the first time; In the second stage, the large-scale neighborhood search algorithm is applied to destroy and reorganize the distribution route obtained in the first stage according to the objective function to obtain the optimal distribution route. Aiming at the three scenarios of consumer group distribution generated by random simulation, high-density and low-density consumer group distribution in Shenyang, this paper uses MATLAB tools to simulate. Through the analysis of simulation results, the working hours and delivery distances of takeout riders are reduced, and the maximum delivery volume is limited. So the method proposed in this paper can not only improve the intelligent level of distribution, but also propose a more humanized distribution mode from the perspective of takeout riders, which greatly supports the sustainable development of the delivery system of network connected takeout platform.