A rolling-horizon decision framework for integrating relocation and user flexibility in one-way electric carsharing systems

Abstract

One-way electric carsharing system has become the most widely used form of carsharing service in practice. However, the demand uncertainty, car imbalance, parking shortage, and battery charging jointly impose great challenges upon the operation. Many carsharing operators, e.g., GoFun, offer incentives to flexible users for accepting alternative itineraries and also adopt a full reservation policy. In this paper, we propose a rolling-horizon decision framework that integrates the operator’s car and staff relocation decisions, user flexibility, and charging of electric cars in the real-time setting. We formulate the problem at each decision epoch as a time–space network flow model that simultaneously optimizes the decisions. We develop an iterated local search heuristic algorithm to solve the model for large-scale practical problems. Based on theoretical insights and clustering of stations, we develop new local search procedures customized for the dynamic carsharing problem. We use real-world data from GoFun as an example to conduct extensive computational experiments. The results show that our algorithm significantly outperforms a particle swarm optimization algorithm and the popular greedy policy, and also has a low efficiency loss compared to the off-line heuristic solution. The transportation network size, clustering size, and operational efficiency are revealed to have significant impacts on the profit and service level.