When optimizing problems with uncertain parameter values in a linear objective, decision-focused learning enables end-to-end learning of these values. We are interested in a stochastic scheduling problem, in which processing times are uncertain, which brings uncertain values in the constraints, and thus repair of an initial schedule may be needed. Historical realizations of the stochastic processing times are available. We show how existing decision-focused learning techniques based on stochastic smoothing can be adapted to this scheduling problem. We include an extensive experimental evaluation to investigate in which situations decision-focused learning outperforms the state of the art for such situations: scenario-based stochastic optimization.

翻译：在具有不确定参数值的线性目标优化问题中，决策导向学习能够实现这些参数的端到端学习。我们关注一类随机调度问题，其中加工时间存在不确定性，导致约束条件中的数值不确定，因此可能需要对初始调度方案进行修复。已知随机加工时间的历史实现数据可用。我们展示了如何将基于随机平滑的现有决策导向学习技术适配到该调度问题中。通过大量实验评估，我们探究了决策导向学习在何种情况下优于当前同类问题的最优方法——基于场景的随机优化。