A three-stage vehicle-cargo supply-demand matching model for online freight platforms

ZHAO Shuang; WEI Qing

doi:10.3969/j.issn.1672-0032.2025.06.003

Abstract：To improve vehicle-cargo matching efficiency on online freight platforms,a three-stage supply-demand matching model is proposed that accounts for the needs of carriers,shippers,and the platform.Stage 1 constructs a cargo attribute classification model to identify and eliminate conflicts among different cargo attributes.Stage 2 develops a vehicle information screening model to extract each vehicle's high-frequency delivery regions.Stage 3 formulates a multi-objective matching model with objective functions that maximize carrier profit,minimize shipper cost,and maximize platform revenue.A traditional genetic algorithm is enhanced with a greedy operator,a circle chaotic map,an opposition-based learning strategy,and a simulated annealing mechanism,and the enhanced algorithm is used to solve the model.An empirical analysis is conducted using Guangzhou-origin freight and vehicle postings released by the China Wutong Network.The results indicate that the matching solutions obtained with the improved genetic algorithm outperform those from the standard genetic algorithm in overall loading efficiency and vehicle utilization.Given the same total shipment volume,the improved algorithm completes transport tasks with fewer vehicles,increases the average number of loads per vehicle,and significantly improves load consolidation.It also converges faster and exhibits a smoother,less volatile post-convergence curve.Compared with the standard genetic algorithm,the improved algorithm increases carrier profit by approximately 32.6％,40.6％,48.6％,and 31.3％across four cargo sets,reduces shipper cost by about 3.2％,10.3％,11.5％,and 7.6％,and raises platform revenue by about 0.9％,0.1％,2.4％,and 0.1％,respectively.Overall,the three-stage matching model and the improved genetic algorithm better balance the interests of carriers,shippers,and online freight platforms.