Anticipatory scheduling of synchromodal transport using approximate dynamic programming
研究了同步联运网络中考虑随机需求的集装箱运输调度问题，提出基于近似动态规划和完美信息价值的启发式算法，数值实验表明该方法显著优于传统启发式。
Abstract 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.