A Greedy Approach for Vehicle Routing When Rebalancing Bike Sharing Systems

With the bloom of the sharing economy, bike sharing systems have earned increasing attention, and a great amount of bike sharing systems have been established in major cities. Users of these systems mainly conduct one-way trips, which leads to an unbalanced distribution of the bikes over time and space. The system operators could hire a fleet of vehicles to move bikes among bike stations for rebalancing. We focus on a routing schedule problem for each vehicle used in the rebalancing process and aims to minimize its moving distance. For the problem, we propose a greedy algorithm which can be easily extended to a parallel version. The scheduled route for a vehicle is adapted from the Hamiltonian path covering all unbalanced bike stations. The algorithm greedily adjusts the route if the vehicle cannot moving along the Hamiltonian path due to capacity limitation violation. Different from previous approaches, our algorithm has a more flexible tradeoff between running time consumption and optimality of the output. Finally, we conduct experiments on both real-world and synthetic datasets and compare the performance of our algorithm with a classic approach.