This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be computed in very short time and under imperfect information. The problem is of importance in various applications where tactical and operational planning problems are interrelated and information about the operational problem is revealed over time. This is for instance the case in certain capacity planning and demand management systems. We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm. The training data set consists of a large number of deterministic (second stage) problems generated by controlled probabilistic sampling. The labels are computed based on solutions to the deterministic problems (solved independently and offline) employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application in load planning for rail transportation show that deep learning algorithms produce highly accurate predictions in very short computing time (milliseconds or less). The prediction accuracy is comparable to solutions computed by sample average approximation of the stochastic program.

翻译：本文为机器学习和操作研究的交叉点提供了方法上的贡献。 也就是说, 我们提出一种方法, 快速预测对特定操作问题的战术解决方案。 在这方面, 战术解决方案不如操作解决方案详细, 但必须在非常短的时间和不完善的信息下进行计算。 这个问题在各种应用中都很重要, 因为战术和操作规划问题相互关联, 有关操作问题的信息会随着时间推移而暴露出来。 例如, 在某些能力规划和需求管理系统中就属于这种情况。 我们把这个问题设计成一个两阶段最佳预知性预知程序, 我们用监督的机器学习算法预测其解决方案。 培训数据集包含大量由受控概率抽样产生的确定性( 第二阶段) 问题。 标签的计算依据是确定性问题( 独立和离线) 的解决方案, 使用适当的集成和分选方法来解决不确定性。 我们在铁路运输工作量规划中的激励应用结果显示, 深度学习算法在非常短的计算时间( 毫或更短的时间) 产生非常准确的预测。 预测准确性精确性可以与以样本平均估计的精确性程序计算方法计算解决方案相比。