Business process simulation is a versatile technique for quantitative analysis of business processes. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, various authors have proposed to discover simulation models from process execution logs, so that the resulting simulation models more closely match reality. However, existing techniques in this field make certain assumptions about resource behavior that do not typically hold in practice, including: (i) that each resource performs one task at a time; and (ii) that resources are continuously available (24/7). In reality, resources may engage in multitasking behavior and they work only during certain periods of the day or the week. This article proposes an approach to discover process simulation models from execution logs in the presence of multitasking and availability constraints. To account for multitasking, we adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original times. Meanwhile, to account for availability constraints, we use an algorithm for discovering calendar expressions from collections of time-points to infer resource timetables from an execution log. We then adjust the parameters of this algorithm to maximize the similarity between the simulated log and the original one. We evaluate the approach using real-life and synthetic datasets. The results show that the approach improves the accuracy of simulation models discovered from execution logs both in the presence of multitasking and availability constraints.

业务流程模拟是一种用于业务流程定量分析的通用技术。过程模拟的一个众所周知的限制是模拟结果的准确性受到过程模型的忠实度和作为模拟器输入的模拟参数的限制。为了解决这个限制，许多作者提出从流程执行日志中发现模拟模型，以便生成的模拟模型更接近现实。然而，该领域的现有技术对资源行为做出了某些在实践中通常不成立的假设，包括：(i) 每个资源一次执行一项任务；(ii) 资源持续可用（24/7）。事实上，资源可能会参与多任务处理行为，并且它们仅在一天或一周的某些时间段内工作。本文提出了一种在存在多任务和可用性约束的情况下从执行日志中发现流程模拟模型的方法。为了解决多任务问题，我们调整任务的处理时间，使得多任务任务按照调整后的时间顺序执行相当于与原始时间并发执行。同时，为了考虑可用性限制，我们使用一种算法来从时间点集合中发现日历表达式，以从执行日志中推断资源时间表。然后我们调整该算法的参数以最大化模拟日志与原始日志之间的相似性。我们使用现实生活和合成数据集评估该方法。结果表明，在存在多任务和可用性约束的情况下，该方法提高了从执行日志中发现的仿真模型的准确性。