Mathematical Modeling of Ride-Hailing Matching Considering Uncertain User and Driver Preferences: Interval-Valued Fuzzy Approach

This study introduces a fuzzy interval-valued approach using multi-objective linear programming to optimize passenger–driver matching in ride-hailing systems, addressing uncertainties in waiting times and fare preferences. The model aims to minimize total waiting times, balance job allocations among drivers, and reduce deviations in fare expectations between passengers and drivers. The proposed framework effectively manages operational uncertainties by incorporating interval-valued fuzzy parameters, including traffic variability and fluctuating demand patterns. Numerical experiments using real-world data demonstrate that the interval-valued fuzzy model significantly outperforms deterministic methods in reducing average waiting times, achieving higher request fulfillment rates, and ensuring a more equitable distribution of assignments among drivers. The results highlight the model’s robustness and adaptability, particularly under high uncertainty scenarios, and its ability to maintain service reliability and user satisfaction. While computational complexity remains a limitation, integrating the model with AI and IoT technologies offers promising avenues for scalability and real-time applications. These findings contribute to advancing optimization frameworks in ride-hailing systems, emphasizing uncertainty management’s importance in enhancing operational efficiency and fairness.