Anticipatory scheduling of synchromodal transport using approximate dynamic programming

We study the problem of scheduling container transport in synchromodal networks considering stochastic demand. In synchromodal networks, the transportation modes can be selected dynamically given the actual circumstances and performance is measured over the entire network and over time. We model this problem as a Markov Decision Process and propose a heuristic solution based on Approximate Dynamic Programming (ADP). Due to the multi-period nature of the problem, the one-step look-ahead perspective of the traditional approximate value-iteration approach can make the heuristic flounder and end in a local-optimum. To tackle this, we study the inclusion of Bayesian exploration using the Value of Perfect Information (VPI). In a series of numerical experiments, we show how VPI significantly improves a traditional ADP algorithm. Furthermore, we show how our proposed ADP-VPI combination achieves significant gains over common practice heuristics.

Automated summary

This study focuses on scheduling container transport in synchromodal networks with stochastic demand. By utilizing the Value of Perfect Information (VPI) and Bayesian exploration, the proposed ADP-VPI combination significantly enhances traditional ADP algorithms and achieves significant gains over common practice heuristics through a series of numerical experiments.