One-to-many two-sided matching decision of logistics O2O platform considering the intermediary benefit

In existing logistics online-to-offline (O2O) platform decision activities, psychological behavioral behaviors of logistics demanders and suppliers have become increasingly complex, and the required information also involves a great deal of uncertainty and vagueness. Hence, a two-sided matching (TSM) method considering multiple psychological behavioral behaviors and the intermediary benefit is proposed. First, a one-to-many supply–demand matching problem on online platform with multiple fuzzy preferences is described. To solve this problem, a novel weight-solving algorithm considering subjectivity and objectivity is developed. Matching utility values are determined according to the prospect theory and attribute weights. Based on desired matching numbers between logistics supply and demand agents and matching utility values, a unilateral expected matching model is constructed. Then, the expected matching matrix is obtained. By using regret theory, agent satisfaction is calculated according to the expected matching matrix. A novel formula considering risk preference coefficients is developed to calculate the intermediary benefit. Moreover, a one-to-many TSM model is established to maximize agent satisfactions of two-sided agents and the intermediary benefit. To obtain a reasonable logistics service matching scheme, a novel max–min algorithm considering fairness is developed. Finally, the rationality, effectiveness and practicality of the proposed method are verified through a one-to-many matching example on a logistics O2O platform.