Code and Data Repository for A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

This repository provides an end-to-end solution to the static rebalancing operations in bike sharing systems. The study uses the raw data of bike demand and station inventory status to generate the optimal rebalancing routes that reallocate system-wide bike inventories among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. The repository reports the raw data, algorithms, and extensive numerical experiments reported in the paper, using real-world data from New York City Citi Bike.