A Hybrid Intelligent Method in a Consolidated Distribution Scheduling

In a consolidated distribution system, the scheduling optimization problem on multi-products for processing time, distribution time and stocking cost is very complex. This paper considers a hybrid optimization method to solve a consolidated distribution scheduling problem. Firstly, a multi-objective integrated optimization model is established besides time and cost factors, specially including the measure of carbon emissions during the distribution. Because of NP-hard in solving the problem, A hybrid intelligent algorithm which is combined from particle swarm algorithm, particle swarm and ant colony algorithm are proposed. Through designing this mixed Intelligent algorithm, the information on the directions and memory in particles can be retained during the updating process. Finally, the real data from State Grid Tianjin electric power materials company is collected when the logistics scheduling in electrical maintenance occurs. Through the simulation experiments, the comparative analysis with the other methods is performed, and the effectiveness of the mixed intelligent algorithm is verified.