Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Robust Repositioning to Counter Unpredictable Demand in Bike Sharing Systems / 3096
Supriyo Ghosh, Michael Trick, Pradeep Varakantham

Bike Sharing Systems (BSSs) experience a significant loss in customer demand due to starvation (empty base stations precluding bike pickup) or congestion (full base stations precluding bike return). Therefore, BSSs operators reposition bikes between stations with the help of carrier vehicles. Due to unpredictable and dynamically changing nature of the demand, myopic reasoning typically provides a below par performance. We propose an online and robust repositioning approach to minimise the loss in customer demand while considering the possible uncertainty in future demand. Specifically, we develop a scenario generation approach based on an iterative two player game to compute a strategy of repositioning by assuming that the environment can generate a worse demand scenario (out of the feasible demand scenarios) against the current repositioning solution. Extensive computational results from a simulation built on real world data set of bike sharing company demonstrate that our approach can significantly reduce the expected lost demand over the existing benchmark approaches.