This paper investigates a dynamic and stochastic shipment matching problem faced by network operators in hinterland synchromodal transportation. We consider a platform that receives contractual and spot shipment requests from shippers, and receives multimodal services from carriers. The platform aims to provide optimal matches between shipment requests and multimodal services within a finite horizon under spot request uncertainty. Due to the capacity limitation of multimodal services, the matching decisions made for current requests will affect the ability to make good matches for future requests. To solve the problem, this paper proposes an anticipatory approach which consists of a rolling horizon framework that handles dynamic events, a sample average approximation method that addresses uncertainties, and a progressive hedging algorithm that generates solutions at each decision epoch. Compared with the greedy approach which is commonly used in practice, the anticipatory approach has total cost savings up to 8.18% under realistic instances. The experimental results highlight the benefits of incorporating stochastic information in dynamic decision making processes of the synchromodal matching system.

本文研究了腹地同步运输中网络运营商面临的动态随机货运匹配问题。我们考虑建立一个平台，该平台可以接收托运人的合同和现货装运请求，并接收承运人的多式联运服务。该平台旨在在现货请求不确定的情况下，在有限范围内提供货运请求和多式联运服务之间的最佳匹配。由于多式联运服务的容量限制，针对当前请求做出的匹配决策将影响对未来请求进行良好匹配的能力。为了解决这个问题，本文提出了一种预期方法，该方法由处理动态事件的滚动范围框架、解决不确定性的样本平均近似方法以及在每个决策时期生成解决方案的渐进对冲算法组成。与实践中常用的贪婪方法相比，预期方法在实际情况下总成本节省高达 8.18%。实验结果凸显了在同步匹配系统的动态决策过程中结合随机信息的好处。