Vehicle Scheduling Problem of Logistics Companies Under Genetic Tabu Hybrid Algorithm

Abstract

Vehicle Scheduling Problem (VSP) plays a very important role in the logistics distribution process. This paper proposes a two-stage algorithm. In the first stage, the fuzzy clustering algorithm is used to divide the customer group into multiple small-scale groups according to different clustering index factors. In the second stage, a new genetic tabu hybrid algorithm was used to solve the vehicle scheduling scheme in each customer class. In addition, a new heuristic crossover operator and inversion mutation operator are used in the genetic tabu hybrid algorithm, which can not only preserve the excellent genes of the parent chromosome, but also generate new individuals quickly. Finally, H Logistics Company is used as an analysis case to solve the optimal distribution plan for 120 customer points. Comparing the results obtained by the algorithm described in this paper with the traditional genetic algorithm, it is concluded that the genetic tabu hybrid algorithm is better than the traditional genetic algorithm.