A vehicle-cargo matching method considering compromise psychology, matching willingness, and heterogeneous information

The highway freight transportation industry often faces high rates of empty truckloads and wastage of transportation resources, especially with empty return trips being common. To address the two-sided matching (TSM) in logistics information platforms, this study proposes a novel method for one-to-one vehicle-cargo TSM considering compromise psychology, matching willingness, and heterogeneous evaluation information. First, the best-worst method and maximum deviation method are utilized to calculate the weights of expectation indicators and reliability indicators. The heterogeneous information contains hesitant fuzzy linguistic term sets (HFLTSs), intervals, and real numbers. HFLTSs are transformed into Gaussian trapezoidal clouds. Then, a generalized VIKOR is employed to calculate the satisfaction levels of expectation indicators and reliability indicators, thereby obtaining satisfaction levels of carriers and shippers in the bilateral relationship. Then, a novel approach is developed for calculating the matching willingness coefficient. A multi-objective programming model is established through maximizing the satisfactions of carriers and shippers and maximizing the revenues for carriers. A new lexicographic sorting algorithm is devised to handle this model to acquire the optimal vehicle-cargo matching solution. Finally, a real-life case of vehicle-cargo TSM for a logistics online-to-offline enterprise demonstrates the application and efficacy of the developed TSM method.