面向随机可修复调度的场景学习
Learning From Scenarios for Stochastic Repairable Scheduling
AI总结：
针对处理时间不确定、需修复初始调度的随机可修复调度问题，提出将基于随机平滑的决策聚焦学习技术适配至该问题，并通过实验验证其在部分场景下优于基于场景的随机优化方法。
摘要：
在优化线性目标中参数值不确定的问题时，决策聚焦学习可实现这些参数值的端到端学习。我们关注一类随机调度问题，其处理时间具有不确定性，这会导致约束中出现不确定值，因此可能需要对初始调度进行修复。目前已有随机处理时间的历史实现数据可用。我们展示了如何将现有基于随机平滑的决策聚焦学习技术适配到该调度问题中。我们开展了全面的实验评估，以探究在哪些场景下决策聚焦学习的表现优于该场景下的现有最优方法：基于场景的随机优化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
