← 研踪 PaperTrailarXiv:2611.14371 · v2
Reliable and explainable analysis of AVS/RS by Statistical Model Checking and Process Mining
首次提交 2026-11-11 01:28
版本更新 2026-11-11 20:40
雷达观测 2026-11-13 11:11
摘要
Automated Vehicle Storage and Retrieval Systems (AVS/RS) are increasingly adopted to boost warehouse throughput while containing operational costs. We present a tool-supported analysis workflow that combines discrete-event simulation, Statistical Model Checking (SMC), and Process Mining (PM) techniques to quantify and explain AVS/RS performance. SMC, implemented through the MultiVeStA tool, delivers statistically reliable estimates of key indicators, such as cycle times and inventory consolidation, across alternative stocking and picking policies with user-specified confidence levels. MultiVeStA orchestrates multiple independent simulations in a black-box manner, enabling automated verification. PM ingests the resulting event logs to uncover the behavioural patterns and bottlenecks that drive those indicators, closing the interpretability gap typical of simulation studies. We instantiate the framework on a new open-source AVS/RS simulator configured according to recent industrial guidelines and evaluate multiple control strategies and different scenarios. Results show that the coordinated use of SMC and PM supports both predictive what-if analyses and post-hoc diagnostics. To the best of our knowledge, this is the first application of a combined SMC-PM workflow to AVS/RS operations, complemented by a reusable open-source simulation and analysis toolchain.
作者备注：66 pages, 11 figures. Author accepted manuscript for Expert Systems with Applications, revised following peer review. Replaces the original pre-review version
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v2 · 2026-11-11 20:40 · 最新观测版本
Reliable and explainable analysis of AVS/RS by Statistical Model Checking and Process Mining
Automated Vehicle Storage and Retrieval Systems (AVS/RS) are increasingly adopted to boost warehouse throughput while containing operational costs. We present a tool-supported analysis workflow that combines discrete-event simulation, Statistical Model Checking (SMC), and Process Mining (PM) techniques to quantify and explain AVS/RS performance. SMC, implemented through the MultiVeStA tool, delivers statistically reliable estimates of key indicators, such as cycle times and inventory consolidation, across alternative stocking and picking policies with user-specified confidence levels. MultiVeStA orchestrates multiple independent simulations in a black-box manner, enabling automated verification. PM ingests the resulting event logs to uncover the behavioural patterns and bottlenecks that drive those indicators, closing the interpretability gap typical of simulation studies. We instantiate the framework on a new open-source AVS/RS simulator configured according to recent industrial guidelines and evaluate multiple control strategies and different scenarios. Results show that the coordinated use of SMC and PM supports both predictive what-if analyses and post-hoc diagnostics. To the best of our knowledge, this is the first application of a combined SMC-PM workflow to AVS/RS operations, complemented by a reusable open-source simulation and analysis toolchain.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-11-11 01:28
Reliable and explainable analysis of AVS/RS by Statistical Model Checking and Process Mining
Automated Vehicle Storage and Retrieval Systems (AVS/RS) are increasingly adopted to boost warehouse throughput while containing operational costs. We present an integrated analysis framework that couples discrete-event simulation with Statistical Model Checking (SMC) and Process Mining (PM) to quantify and explain AVS/RS performance. SMC, implemented through the MultiVeStA tool, delivers statistically reliable estimates of key indicators, such as cycle times, throughput, and storage utilization, across alternative stocking and picking policies with user-specified confidence levels. MultiVeStA orchestrates multiple independent simulations in a black-box manner, enabling automated verification. PM ingests the resulting event logs to uncover the behavioral patterns and bottlenecks that drive those indicators, closing the interpretability gap typical of simulation studies. We instantiate the framework on a new open-source AVS/RS simulator configured according to recent industrial guidelines and evaluate multiple control strategies. Results show that the joint use of SMC and PM enables both predictive what-if analyses and post-hoc diagnostics, providing the first combined SMC+PM assessment of AVS/RS operations and a reusable toolchain for automated warehousing research.
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