Survey of vehicle-cargo matching models and algorithms
With the rapid increase in truck ownership and freight demand,vehicle-cargo matching problem has become the core and hotspot of freight e-commerce platforms.In this study,we mainly focused on the vehicle-cargo matching problem to sort out and summarize the existing literature from both model and algorithm aspects.In terms of the vehicle-cargo matching model,this study is based on the bilateral matching theory,which is designed in accordance with the satisfaction,fair-ness,and stability of the three aspects of the evaluation index.Based on the application scenarios,ve-hicle-cargo matching models can be divided into three categories:one-to-one vehicle-cargo matching model,one-to-many vehicle-cargo matching model,and many-to-many vehicle-cargo matching mod-el.Here,the one-to-one vehicle-cargo matching model is for the entire truck transportation,and the other two correspond to the less-than-truckload(LTL)transportation.The complexity of the three ap-plication scenarios is increasing,and the corresponding difficulty and time taken to solve the prob-lems also show an increasing trend.In terms of vehicle-cargo matching algorithms,the data can be divided into four categories based on the structure and characteristics of freight demand:optimiza-tion algorithms,artificial intelligence algorithms,recommendation algorithms,and other algorithms.For small-scale freight transportation needs that do not require high timeliness,optimization algo-rithms are often used,and the corresponding solution process and architecture are designed accord-ing to the characteristics and features of the model.For big data,interactive data or real-time match-ing requirements of freight transportation needs,artificial intelligence algorithms,and recommenda-tion algorithms are effective methods for the vehicle-cargo matching problem,in which artificial in-telligence algorithms can be classified according to the owner's behavior or matching results,and recommendation algorithms can effectively recall and recommend the big data for vehicle and cargo information.In the end,the shortcomings of previous studies are summarized,and three directions for further research are highlighted.First,the practical feasibility of the method for trucks and cargo matching should be improved by combining the actual business scenarios and the background of big data of truck and cargo information.The second is the further investigation of evaluation indexes,such as the preference of vehicle owners,and the probability of receiving orders at the order destina-tion.The third is the real-time decision making for vehicle and cargo matching,with a focus on the dynamic randomness of freight demand and the goal of long-term benefits for platforms and owners.
highway transportationfreight e-commerce platformvehicle and cargo matching mod-elsvehicle and cargo matching algorithms
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