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：当优化问题在目标函数中存在不确定参数时，决策聚焦学习能够实现对这些参数的端到端学习。我们研究一个随机调度问题，其中处理时间具有不确定性，这导致约束条件中存在不确定值，因而可能需要对初始调度方案进行修复。我们可获得随机处理时间的历史实现数据。本文展示了如何将基于随机平滑的现有决策聚焦学习技术适配于此调度问题。我们进行了广泛的实验评估，以探究在何种情况下决策聚焦学习能够超越此类情境下的现有最优方法：基于场景的随机优化。