One of the critical issues in the operation of vehicle-sharing systems is the optimization of the fleet repositioning movements. Repositioning implies the artificial movement of vehicles from places where they accumulate to others in which they are scarce. This yields a higher vehicle availability, without over dimensioning the vehicle fleet and while increasing the vehicle utilization rates. In the particular case of bike-sharing systems, repositioning implies to deploy a fleet of small trucks or vans able to move groups of bicycles from one location to another, with the purpose of maximizing the users’ level of service while minimizing the operating agency costs. This repositioning optimization problem has been previously addressed in the operations research field through Mixed Integer Programing (MIP) and its variants, generally facing two limitations. First, its high computational cost, which prevents achieving direct solutions in realistically large systems. So, it has been necessary to develop heuristics and approximations. And second, its reliance and sensitivity to demand forecasts, with its inherent level of uncertainty. Aiming to overcome these weaknesses, this paper presents a strategy based on a real-time pairwise assignment between repositioning trucks and tasks, in order to optimize the bike-sharing repositioning operations. The proposed method is conceptually simple, less dependent on demand predictions, easily implementable in any coding language and applicable to large systems at a low computational cost. These properties make the method appealing to address the repositioning task assignment in any vehicle-sharing system. On a simulated case study, based on Bicing, the bicycle-sharing system in Barcelona, the proposed strategy has been implemented and compared to the MIP-based routing approach. Results show that the proposed real-time pairwise assignment method is able to significantly improve the performance of the repositioning operations, especially in scenarios where the demand forecast is not accurate. Being based on real-time information, the proposed strategy is flexible enough to solve unpredictable situations. So, the proposed strategy can be implemented as an alternative to MIP-based solutions, or as a complementary strategy for dynamic real-time adaptation of static long-term solutions.
