p‐hub median location optimization of hub‐and‐spoke air transport networks in express enterprise

Abstract

Summary Based on the single allocation p‐hub median location problem in hub‐and‐spoke networks, this paper studies the aviation hubs location optimization of express enterprise, primarily using p‐median model and greedy dropping heuristic algorithm to solve the initial candidate locations of the aviation hubs by Lingo. Then, the integer program model of the location and allocation of the aviation hubs was set up, with the upper limit of the distribution distance as the main constraint. The improved locations were calculated by immune algorithm on Matlab. The numerical example of A express enterprise was presented. After analyzing the status and the problems of aviation hub location in East China, the two best hub locations Wuxi and Hangzhou were finally determined by above model with the total cost reduced by 19.6%. The optimization results show the validation of the model.

Special issue on multimodal information learning and analytics on cross‐media big data

“…An integrated fault diagnosis reasoning strategy based on fusing rough sets, neural network, and evidence theory is presented by Yang and Yu using the principles of data fusion and meta‐synthesis. Zhang et al study the aviation hubs location optimization of express enterprise, primarily using p‐median model and greedy dropping heuristic algorithm to solve the initial candidate locations of the aviation hubs by Lingo. Yong et al considered the construction of evaluation index system according to the concept and construction situation of Smart City, adopted the five‐year statistics of Sichuan Province as empirical data, and this paper applied modeling calculation to draw the objective state of current Smart City construction.…”