Abstract: Aiming at the problems of low level, low efficiency and high cost of the vehicle logistics in manufacturing enterprises, a mathematical model for collaborative optimization vehicle allocation and route is established. The model considers a variety of factors,including heterogeneous vehicles, cargo types, vehicle queuing and vehicle quantity. In addition, the logistics completion time,vehicle mileage and logistics cost are employed as the objectives of the model. To solve the model effectively, a hybrid discrete sparrow search algorithm is proposed. In the algorithm, an initialization method based on reverse learning is used to improve the diversity of the population, several discretization strategies are designed to accommodate the discrete characteristics of the problem.Moreover, a variety of local search strategies are designed to improve the search ability, and a solution retention strategy is designed to avoid the algorithm falling into local optimization. By analyzing an actual case of a cement enterprise, the effectiveness of the proposed strategies and the superiority of the proposed algorithm are verified. The obtained logistics scheme can effectively reduce the logistics cost by 11%, improve the logistics efficiency by 19.8%, and reduce the vehicle mileage by 29.1%.

Key words: heterogeneous vehicle routing, vehicle allocation, hybrid discrete sparrow search algorithm, multi-objective optimization, vehicle queuing