Periodic Freight Demand Estimation for Largescale Tactical Planning
Abstract
Freight carriers rely on tactical planning to design their service network to satisfy demand in a costeffective way. For computational tractability, deterministic and cyclic Service Network Design (SND) formulations are used to solve largescale 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 decisionmaking 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 largescale 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.
 Publication:

arXiv eprints
 Pub Date:
 May 2021
 DOI:
 10.48550/arXiv.2105.09136
 arXiv:
 arXiv:2105.09136
 Bibcode:
 2021arXiv210509136L
 Keywords:

 Computer Science  Machine Learning;
 Mathematics  Optimization and Control