Research on routing-loading cooperative optimization under uncertain demand environment

Under the uncertain demand environment, for the problem of cyclic pickup, a collaborative optimization model based on VRP and 3D-KLP (3KL-CVRPCSO) was proposed, and a multi-stage algorithm (HPGBT) to solve the model was designed. First, a hybrid particle swarm optimization algorithm based on genetic algorithm and a heuristic orthogonal binary tree search algorithm are used to solve the optimal driving routing of different vehicle types and the optimal quantity of various types of goods loaded in the compartment. In this way, the optimal single-vehicle routing-loading plan of each type of vehicle is determined; and these plans are used as decision variables, and the actual cargo demand is the constraint condition, a new routing-loading collaborative optimization model based on actual demand is established and solved, and the result is that the number of vehicles executed according to the optimal plan of different models of bicycles. By comparing with the optimization results of foreign scholars in authoritative journals and the application of actual cases, the research and verification of two aspects have proved the feasibility and effectiveness of this method. Thus, a new method of logistics vehicle scheduling is established that takes the "routing + loading" composite plan of different models of bicycles as the decision-making unit, takes the actual demand as the constraint, and then uses the optimal combination plan to solve the uncertain demand problem. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.