arXiv:2410.16941cs.LG2024-10被引 3

用概率模型同时刻画资源间歇可用性和多任务行为,提升流程仿真准确性。

Business Process Simulation: Probabilistic Modeling of Intermittent Resource Availability and Multitasking Behavior

  • 为每个时间槽分配可用概率和多任务概率,实现动态建模。
  • 基于事件日志发现概率化日历与多任务能力,仿真结果更贴近真实分布。
  • 适合研究流程仿真、资源调度或实际业务系统建模的读者。

在业务流程仿真中,资源可用性通常通过为每个资源分配日历(如周一至周五,9:00-18:00)来建模,假设资源在日历内每个时间段都始终可用。然而,中断、休息或跨流程分时使用会导致这一假设失效,现有方法难以捕捉间歇性可用性。另一局限是:要么忽略多任务行为,要么假设资源在可用时总是多任务(最多到容量上限)。但研究表明,多任务模式随天数变化。本文提出一种概率化方法,为资源日历中的每个时间槽赋予可用概率和不同多任务等级下的多任务概率。例如,某资源在周五14:00-15:00有90%可用概率,若已执行一项任务,则60%概率可承接第二项并发任务。我们提出了从事件日志中挖掘概率化日历和概率化多任务能力的算法。评估表明,相比采用确定性日历和单任务假设的方法,该模型能更准确复现活动分布与周期时间分布。

原文摘要 · Abstract (English)

In business process simulation, resource availability is typically modeled by assigning a calendar to each resource, e.g., Monday-Friday, 9:00-18:00. Resources are assumed to be always available during each time slot in their availability calendar. This assumption often becomes invalid due to interruptions, breaks, or time-sharing across processes. In other words, existing approaches fail to capture intermittent availability. Another limitation of existing approaches is that they either do not consider multitasking behavior, or if they do, they assume that resources always multitask (up to a maximum capacity) whenever available. However, studies have shown that the multitasking patterns vary across days. This paper introduces a probabilistic approach to model resource availability and multitasking behavior for business process simulation. In this approach, each time slot in a resource calendar has an associated availability probability and a multitasking probability per multitasking level. For example, a resource may be available on Fridays between 14:00-15:00 with 90\% probability, and given that they are performing one task during this slot, they may take on a second concurrent task with 60\% probability. We propose algorithms to discover probabilistic calendars and probabilistic multitasking capacities from event logs. An evaluation shows that, with these enhancements, simulation models discovered from event logs better replicate the distribution of activities and cycle times, relative to approaches with crisp calendars and monotasking assumptions.

流程仿真概率建模资源调度

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