Efficient inventory routing for Bike-Sharing Systems: A combinatorial reinforcement learning framework

Abstract

Bike-sharing systems have become increasingly popular, providing a convenient, cost-effective, and environmentally friendly transportation option for urban commuters on short trips. However, an efficient and sustainable bike-sharing system faces a key challenge to dynamically balancing the supply and demand of bicycles through efficient inventory routing. This paper introduces a comprehensive combinatorial framework that tackles the critical challenges in the bike-sharing system's inventory routing problem. Firstly, we present a novel mathematical model that considers multiple delivery vehicle types and incorporates important factors like dispatch cost, service time, and user satisfaction, all while ensuring fair scheduling. The comprehensiveness of our model makes it highly applicable to real-world scenarios, addressing practical concerns faced by bike-sharing companies. Secondly, we leverage reinforcement learning mechanisms to gather quantitative information on the spatial and temporal patterns of demand and supply. With this data, we construct an effective regression model that accurately predicts station demand. Additionally, we propose an efficient heuristic approach to generate service sequences for delivery vehicle dispatching. Our approach employs a far-sighted strategy-based local iterative search algorithm to construct solutions, coupled with an adaptive exploration algorithm to continually improve solution quality. The proposed solution method is an innovative integration of reinforcement learning, demand prediction, and heuristic-based dispatching, significantly enhancing solution quality and computational efficiency. By bridging the gap between academic research and real-world practice, our framework offers practical and effective solutions for bike-sharing systems. Finally, we validate our proposed framework with extensive experimental results using real-world datasets. Our approach outperforms state-of-the-art algorithms within a short computational time, demonstrating its superiority in terms of solution quality compared to prior literature. Our research opens a new, viable direction for industrial practice, providing valuable insights for decision-makers to optimize bicycle inventory management in a smarter and more efficient way.