控制流不确定性下的业务流程规划与调度
Planning and Scheduling Business Processes under Control-Flow Uncertainty: Extended Version
- Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对业务流程中控制流不确定导致调度困难的问题，提出机会约束优化框架，包含分解与集成两种方法，在真实和合成数据上验证了集成方法更优但不可扩展，分解方法可扩展。
AI中文摘要：
业务流程中的活动调度可以提高效率（例如，减少完工时间），但由于执行过程中基于数据做出的决策，完成一个案例所需的确切活动序列往往是不确定的，因此调度具有挑战性。尽管如此，关于此类决策的概率信息通常可以从历史执行日志中估计或推导出来，并有助于预测哪些执行路径可能导致成功完成。针对特定执行路径进行规划会影响可行性（即成功完成的概率）以及计划但从未执行的冗余活动的预期数量。我们将该问题构建为机会约束优化问题，并提出两种公式：一种分解方法，包含两个阶段，规划阶段在可行性约束下最小化冗余活动的预期数量，调度阶段在计划活动上最小化完工时间；以及一种集成方法，将规划和调度结合到单一公式中。在两个真实数据集和一个合成数据集上的评估表明，集成方法能产生更优的完工时间，但在大规模下难以处理，而分解方法可扩展到大规模设置。
英文摘要：
Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.
