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.
翻译:货运承运人依靠战术规划来设计其服务网络,以便以具有成本效益的方式满足需求。对于计算牵引性、确定性和周期性服务网络设计(SND)的配方用于解决大规模问题。中央投入是定期需求,即在规划的每个时期,预计需求会重复。实际上,需求是通过一个时间序列预测模型预测的,定期需求是这些预测的平均数。然而,这只是许多可能的绘图中的一种。在文献中一直忽视了选择这种绘图的问题。我们提议使用下游决策问题的结构来选择一个良好的绘图。为此目的,我们采用了一种多层次的数学方案拟订方案,将时间序列预测与SND感兴趣的问题明确联系起来。解决办法是定期需求估算,将战术规划范围的成本降至最低,我们报告对加拿大国家铁路公司大规模应用的结果进行了广泛的实证研究。它们清楚地表明了定期需求估算问题的重要性。事实上,规划成本显示,不同的定期需求估算存在重大差异,甚至使用了与平均需求预测不同的估计值,并且使用比平均需求更好的估计值。使用比平均需求更准确的估计数,因此,规划成本可以大幅变化。