Highlights • Bike inventory determines the service level in bike sharing systems. • Optimizing bike inventory requires considering multiple complex factors. • Efficient simulation approach can replicate the bike flows in large scale systems. • Simulation-optimization heuristics can help optimizing initial bike inventory. • Demand intensity functions drives bike allocation for dynamic repositioning.

Abstract Bike-sharing systems (BSS) are a healthy and environmentally friendly alternative for public transportation in many large urban areas around the world. In 2010, Mexico City started its BSS, named Ecobici, with 84 stations and reached more than 840,000 trips that year. Ecobici rapidly became a preferred option for many users. By 2019, the number of stations in Ecobici had grown almost 6 times since the first year. In addition, the number of registered trips on 2019 was more than 8.4 million and the number of trips in a weekday were more than 30,000. In order to incentivize the adoption of bike as transportation mode, Ecobici and other BSS need to handle the bike inventory at their stations throughout the day. There are many challenges for optimizing bike inventory in BSS given the complex interdependencies, the non-homogeneous demand flows, and the large-scale scenarios. In this paper, we propose and study a simulation–optimization framework to minimize the number of shortage events (failed bike withdrawals and returns) by setting an adequate bike inventory at each station throughout the day. We propose an efficient modeling approach to simulate large-scale BSS and a simulation–optimization heuristic to optimize bike inventory. The experiments show that these algorithms obtain results with similar quality than other search methods, but in a significant shorter amount of time.