Genetic algorithm and dynamic planning based airline shipment optimization system

Abstract

Aiming at the difficult NP problem of box selection and crating in the airline shipment process, this paper constructs an airline shipment optimization system based on genetic algorithm and dynamic planning. It can be solved under multiple constraints to get the cargo loading scheme with the highest utilization of crating space and the highest economic efficiency. In this paper, the cargo is firstly organized according to the average value of the first fifty cycles, and the optimization system is designed to leave as little gap as possible. Considering the container dimension constraint, a container selection optimization model which integrates the container volume, dead weight and space utilization is established, and the optimal container type corresponding to each cargo is solved by genetic algorithm. After that, a trinomial tree structure model is established for the containers, and a spatial partitioning algorithm is designed to solve the packing scheme for the cargoes and further obtain the required number of containers. The optimal system of airline shipment considering the economic efficiency and the reliability of changing cargo source is studied by the combined optimization algorithm and the time series algorithm respectively. The reliability is determined to be 95%, and the conclusions obtained prove that the optimization system proposed in this paper can complete the shipment task allocation work quickly and efficiently.