面向大规模战术规划的周期货运需求估计
Periodic Freight Demand Estimation for Large-scale Tactical Planning
摘要：
货运公司依靠战术规划来设计其服务网络，以符合成本效益的方式满足需求。对于计算的易处理性，确定性和周期性的服务网络设计（SND）配方用于解决大规模的问题。一个中心输入是周期性需求，即在计划范围内的每个周期中预期重复的需求。实际上，需求是通过时间序列预测模型来预测的，而周期需求是这些预测的平均值。然而，这只是许多可能的映射之一。在选择这种映射的问题，迄今被忽视的文献。我们建议使用下游决策问题的结构来选择一个好的映射。为了这个目的，我们引入了一个多层次的数学规划公式，明确链接的时间序列预测的SND感兴趣的问题。该解决方案是一个定期的需求估计，最大限度地减少成本超过战术规划的视野。我们报告的结果在一个广泛的实证研究的大规模应用，从加拿大国家铁路公司。它们清楚地表明了周期性需求估计问题的重要性。实际上，规划成本在不同的周期性需求估计上表现出重要的变化，并且使用不同于平均预测的估计可以导致大幅的成本降低。此外，根据预测进行的定期需求估计所涉费用与使用实际需求平均数得出的费用相当，甚至更高。
英文摘要：
Freight carriers rely on tactical planning to design their service network to satisfy demand in a cost-effective way. For computational tractability, deterministic and cyclic Service Network Design (SND) formulations are used to solve large-scale problems. A central input is the periodic demand, that is, the demand expected to repeat in every period in the planning horizon. In practice, demand is predicted by a time series forecasting model and the periodic demand is the average of those forecasts. This is, however, only one of many possible mappings. The problem consisting in selecting this mapping has hitherto been overlooked in the literature. We propose to use the structure of the downstream decision-making problem to select a good mapping. For this purpose, we introduce a multilevel mathematical programming formulation that explicitly links the time series forecasts to the SND problem of interest. The solution is a periodic demand estimate that minimizes costs over the tactical planning horizon. We report results in an extensive empirical study of a large-scale application from the Canadian National Railway Company. They clearly show the importance of the periodic demand estimation problem. Indeed, the planning costs exhibit an important variation over different periodic demand estimates and using an estimate different from the mean forecast can lead to substantial cost reductions. Moreover, the costs associated with the periodic demand estimates based on forecasts were comparable to, or even better than those obtained using the mean of actual demand.
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