Modelling and Optimization for the Employment of Full-Time and Part-Time Drivers in Ride-Hailing Platforms

The employment strategy is of great significance to improve the performances of the ride-hailing platforms. This paper establishes a nonlinear programming model to characterize the employment problem of a ride-hailing platform where full-time drivers and part-time drivers coexist, facing a month-varied daily market size. The daily online time of the full-time drivers is assumed to be fixed, while the daily online time of the part-time drivers is self-scheduled. The objective of the decision model is to maximize the total effective demand of the ride-hailing platform, and the constraints are to guarantee the minimum income of both the full-time and part-time drivers. By solving the models, we find the decision problem can be simplified as a linear programming model and the optimal numbers of full-time drivers and part-time drivers are derived. Theoretical results show that it is optimal for the ride-hailing platform to employ only part-time drivers if the cap of number of part-time drivers is sufficiently high, and the number of part-time drivers satisfies that the expected income per unit time during the month with the minimum daily market size equals the required minimum income. Otherwise, if the cap of number of part-time drivers is not sufficiently high, full-time drivers and part-time drivers will coexist. Finally, a numerical experiment is conducted to illustrate the theoretical results.