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With the development of the freight platform market, the problem of asymmetric information between vehicles and consignors has been effectively alleviated, and there is still room for improvement in the satisfaction of car owners and consignors, vehicle loading rate, and the cost-borne by consignor. In the context of transportation capacity supply exceeding freight demand, the satisfaction of the consignor becomes the key factor affecting vehicle-cargo matching and platform competitiveness. To make the solution of vehicle-cargo matching more desirable to the requirement of the matching party and attract more customers, a new model is proposed by considering the consignor. The two parties to the vehicle-cargo matching are regarded as the customer side and the service provider involved in logistics services according to the definition of a logistics streamline network. Considering the closeness between the consignor’s requirements for cargo integrity, time and cost of transportation, and the motorists’ actual service of transportation, the matching degree is defined. Then, a matching satisfaction model is constructed to maximize the matching satisfaction, and an improved genetic algorithm is designed and solved using Python. Three different models of vehicle-cargo matching are compared on the data of the platform. The result shows that the average vehicle loading rate of the proposed model is 31.2% higher than the one of the one-to-one matching model, and the cost of the consignor is 4.3% lower. Compared with the one-to-many vehicle-cargo matching model with transportation cost as the target, the average consignor satisfaction increased by 16.2%, and the cost borne by the consignor decreased by 2.9%. It reduces the total cost borne by the consignor and maintains a high consignor satisfaction and vehicle loading rate while being more in line with the actual scenario.
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