In this paper, we are concerned with the automated exchange of orders between logistics companies in a marketplace platform to optimize total revenues. We introduce a novel multi-agent approach to this problem, focusing on the Collaborative Vehicle Routing Problem (CVRP) through the lens of individual rationality. Our proposed algorithm applies the principles of Vehicle Routing Problem (VRP) to pairs of vehicles from different logistics companies, optimizing the overall routes while considering standard VRP constraints plus individual rationality constraints. By facilitating cooperation among competing logistics agents through a Give-and-Take approach, we show that it is possible to reduce travel distance and increase operational efficiency system-wide. More importantly, our approach ensures individual rationality and faster convergence, which are important properties of ensuring the long-term sustainability of the marketplace platform. We demonstrate the efficacy of our approach through extensive experiments using real-world test data from major logistics companies. The results reveal our algorithm's ability to rapidly identify numerous optimal solutions, underscoring its practical applicability and potential to transform the logistics industry.

翻译：本文关注在市场化平台中物流企业间的订单自动交换问题，旨在优化总体收益。我们提出一种新颖的多智能体方法，聚焦于个体理性视角下的协同车辆路径问题（CVRP）。所提出的算法将车辆路径问题（VRP）原理应用于不同物流企业的车辆配对，在考虑标准VRP约束及个体理性约束的同时优化整体路径。通过采用互惠交换方式促进竞争性物流代理间的协作，我们证明该方法能够系统性地缩短运输距离并提升运营效率。更重要的是，我们的方法确保了个体理性与更快的收敛速度，这是保障市场化平台长期可持续性的关键属性。我们利用大型物流企业的真实数据开展大量实验，验证了该方法的有效性。结果表明，该算法能够快速识别大量最优解，突显其实际应用价值及变革物流行业的潜力。