Data underlying the paper: Using agent-based simulation for emergent behavior detection in cyber-physical systems

The dataset contains a collection of experiment results and event logs generated. The experiments comprise a logistics case study involving the transport of products that are subject to quality depreciation. The products are transported on smart pallets, which enables us to keep track of various measures (e.g., location, quality depreciation level, etc.). We considered the problem as a special case of the dynamic pickup and delivery problem in which objects need to be transported between an origin and a destination. The case study is implemented in a discrete-event simulation model. This dataset contains the experiment input (i.e., input parameters) and results (i.e., simulation output files and filtered output files considering the removal of a warm-up period). As output, we also provide event logs, which could, for example, be used for model verification or process mining purposes.

本数据集收录了生成的实验结果与事件日志（event logs）。本次实验涵盖一项物流案例研究，聚焦于易发生质量衰减的产品运输场景。该场景中产品通过智能托盘（smart pallets）完成运输，借此可追踪位置、质量衰减程度等多项运行参数。研究将该运输问题归为动态取送货问题（dynamic pickup and delivery problem）的一类特殊场景，即需在起点与终点之间转运货物的场景。本案例研究基于离散事件仿真模型（discrete-event simulation model）实现。数据集包含实验输入（即输入参数）与实验结果两部分：实验结果涵盖仿真输出文件，以及剔除预热期（warm-up period）后的过滤输出文件。此外，数据集还提供了事件日志，可用于模型验证或流程挖掘（process mining）等相关场景。