Research on Parallel Distribution Routing Optimization Based on Improved Reinforcement Learning Algorithm

With the rapid development of e-commerce, the efficiency requirements for logistics distribution are getting higher and higher, so distribution routing optimisation has important theoretical and practical significance. In this paper, a new coding mechanism is designed to solve the distribution task in the multi-distribution centre scenario and vehicle scheduling problem with capacity constraints in the distribution scenario. Simulation results show that this algorithm has the advantages of high solution efficiency, variable input and avoidance of re-referencing compared with two heuristics of Google OR-Tools, ant colony algorithm and simulated annealing algorithm, thus this algorithm can effectively solve the logistics distribution routing optimisation problem and improve the efficiency of logistics distribution.