Stable matching for long-term carpooling: an approach based on knowledge rules and Benders decomposition
针对长期拼车场景，提出一种基于知识规则和Benders分解的稳定匹配方法，通过公平匹配模型最大化匹配人数，并用数值实验验证了可行性。
Carpooling can help in fully utilising empty seats by matching drivers and riders, thereby effectively improving transportation utilisation efficiency. This study proposes a stable matching method for long-term carpooling. First, it describes the stable matching problem of long-term carpooling (SMLC) and outlines the relevant definitions of fair stable matching. Second, fair and nearly fair stable matching models are constructed to maximise the matching number of carpooling travellers. Subsequently, a heuristic algorithm based on knowledge rules and Benders decomposition is proposed. Finally, the propose method’s feasibility and effectiveness are verified through numerical experiments.