One of the most critical problems in bike-sharing services is a bicycle repositioning problem, which is how service providers must relocate their bicycles to maintain the quality of service. In this paper, we propose an end-to-end approach for the bike repositioning problem, which realizes the operator-feasible repositioning plan with cooperation among multiple trucks. Our proposed algorithm consists of three procedures. First, we predict the number of rented and returned bicycles at each station with a deep learning based on the bicycle usage information. Second, we determine the optimal number of bicycles to satisfy the availability of each station by solving an integer optimization problem. Finally, we solve the vehicle routing problem formulated as another integer optimization problem. Based on our algorithm, service operators can actually perform a relocation task based with a reference to the truck capacity, routes, and the number of bicycles to be loaded and unloaded. We demonstrate the applicability of our algorithm in the real world through numerical experiments on the real bicycle data of a Japanese company.