Simulation Modelling Practice and Theory最新文献_第7页

A data-driven friction coefficient model and its application in meshing efficiency prediction of heavy-duty gears 数据驱动的摩擦系数模型及其在重型齿轮啮合效率预测中的应用

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-23 DOI: 10.1016/j.simpat.2025.103173

Ningwei Xia , Changjiang Zhou , Shengwen Hou , Fa Zhang

Heavy-duty gears are extensively utilized in high-power equipment such as helicopters, ships, and commercial vehicles, often leading to significant frictional power losses. Accurate friction prediction is essential for designing energy-efficient transmission systems. This study proposes a data-driven model to predict the friction coefficient and applies it to estimate the meshing efficiency of heavy-duty gears. By training on friction test data under various lubrication conditions, an extreme gradient boosting (XGBoost) model is developed to predict the friction coefficient, with hyperparameters optimized through grid search and cross-validation. The model’s decision mechanism is interpreted using Shapley additive explanations, highlighting the influence of speed, load, surface roughness, and lubricant viscosity on the friction coefficient. When applied to predict meshing efficiency, the model is experimentally validated, achieving a maximum prediction error of 0.211 % and an average error of 0.108 %. The effects of major operating and geometrical parameters are analyzed, showing that meshing efficiency increases with higher speeds, torque, pressure angles, tip relief length, and lower addendum coefficients. The results indicate that proper parameter optimization and the use of high-viscosity lubricants can enhance the energy efficiency of heavy-duty gears.

重型齿轮广泛应用于高功率设备，如直升机，船舶和商用车辆，经常导致显著的摩擦功率损失。准确的摩擦预测对设计节能传动系统至关重要。提出了一种数据驱动的摩擦系数预测模型，并将其应用于重载齿轮的啮合效率估算。通过对不同润滑条件下的摩擦试验数据进行训练，建立了预测摩擦系数的极限梯度增压（XGBoost）模型，并通过网格搜索和交叉验证优化了超参数。该模型的决策机制使用Shapley添加剂解释来解释，突出了速度、负载、表面粗糙度和润滑剂粘度对摩擦系数的影响。将该模型用于预测网格效率，并进行了实验验证，最大预测误差为0.211%，平均预测误差为0.108%。分析了主要工作参数和几何参数对啮合效率的影响，表明转速、转矩、压力角、叶尖卸荷长度和齿顶系数越小，啮合效率越高。结果表明，适当的参数优化和高粘度润滑剂的使用可以提高重型齿轮的能量效率。

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引用次数: 0

Simulating optimal flood evacuation using heuristic algorithms and path-choice behaviors 利用启发式算法和路径选择行为模拟最优洪水疏散

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-23 DOI: 10.1016/j.simpat.2025.103167

Housseyn Chebika , Guoqiang Shen , Haoying Han , Mahmoud Mabrouk , Brahim Nouibat

Effective path planning in flooding emergency rescue scenarios is essential for ensuring timely evacuation while minimizing safety risks. Conventional path-planning algorithms often prioritize the shortest or most cost-efficient routes, potentially neglecting safety considerations. To address this limitation, this study introduces an improved path-planning method using a behavior-based A-star (A*) algorithm designed for evacuation scenarios. A cellular automata (CA) environment is applied to address common challenges associated with traditional A* algorithms, including path inefficiencies, longer distances, and difficulties in handling dynamic flood environments. The key innovation of this study is the optimization of a heuristic function by integrating depth sensitivity perception (DSP), which directly influences evacuation behavior by prioritizing safer paths based on real-time water depth assessments during path selection. Experimental results across diverse flood scenarios demonstrate that the optimized A* algorithm significantly outperforms traditional A-star and Dijkstra’s algorithms, achieving reductions in explored nodes by 90.06 % and 93.13 %, lowering safety risks, and shortening computational times by 87.65 % and 88.06 %, respectively. These findings validate the efficacy of the depth-sensitive heuristic in enhancing evacuation pathfinding within complex flood environments.

在洪水紧急救援场景中，有效的路径规划对于确保及时疏散和最大限度地降低安全风险至关重要。传统的路径规划算法通常优先考虑最短或最具成本效益的路线，潜在地忽略了安全考虑。为了解决这一限制，本研究引入了一种改进的路径规划方法，该方法使用基于行为的a -star （a *）算法，该算法专为疏散场景设计。应用元胞自动机（CA）环境来解决与传统A*算法相关的常见挑战，包括路径效率低下、距离较长以及处理动态洪水环境的困难。本研究的关键创新点是通过集成深度敏感感知（DSP）优化启发式函数，该函数在路径选择过程中基于实时水深评估来优先考虑更安全的路径，直接影响疏散行为。不同洪水场景下的实验结果表明，优化后的A*算法显著优于传统的A-star和Dijkstra算法，探测节点减少了90.06%和93.13%，降低了安全风险，计算时间缩短了87.65%和88.06%。这些发现验证了深度敏感启发式算法在复杂洪水环境中增强疏散寻路的有效性。

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引用次数: 0

Machine learning methods in microscopic pedestrian and evacuation dynamics simulation: a comparative study 微观行人和疏散动力学模拟中的机器学习方法：比较研究

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-05 DOI: 10.1016/j.simpat.2025.103180

Nan Jiang , Hanchen Yu , Eric Wai Ming Lee , Hongyun Yang , Lizhong Yang , Richard Kwok Kit Yuen

The modeling and simulation of pedestrian and evacuation dynamics provides essential insights for the field of crowd safety against the background of population increasing and regional development. With the superior performance of machine learning methods demonstrated in pedestrian modeling, varying data encoding schemes and machine learning algorithms were investigated and lack of comparative analysis. Hence, this study analyzes machine learning methods for simulating microscopic pedestrian and evacuation dynamics. The motion interaction field along with a data extraction rule that standardizes input lengths for learning-based models is proposed. Two typical algorithms, Classification and Regression Trees (CART) and Artificial Neural Networks (ANN), are employed for model training and comparison. The fitting performance is evaluated using mean absolute error of velocity, revealing that the CART-based model outperforms the ANN-based model in stability and lower error rates, particularly in varying local density ranges. Dynamics tests are further performed to examine the two models’ robustness against inherent error. The results indicate that the CART-based model struggles under high-density conditions due to the split-based structure. In contrast, the ANN-based model demonstrates superior non-linear fitting ability, allowing for better reproduction of pedestrian dynamics at relatively higher densities. Moreover, the Wasserstein Distance with Sinkhorn iteration is used to quantify model performance in terms of flow-density fundamental diagrams, highlighting the advantages of learning-based approaches over traditional social force model. This research has significant implications for the field of building and civil engineering, as insights from comparative analysis of two typical machine learning algorithms and the establishment of motion interaction field can inform the progress of learning-based pedestrian and evacuation dynamics simulation. The study presented underscores the transformative potential of machine learning methods in simulating pedestrian dynamics and suggests future research directions to enhance robustness and applicability across diverse scenarios of learning-based methods in microscopic pedestrian and evacuation dynamics simulation.

行人和疏散动力学的建模和仿真为人口增长和区域发展背景下的人群安全领域提供了重要的见解。由于机器学习方法在行人建模中表现出优越的性能，研究人员对不同的数据编码方案和机器学习算法进行了研究，但缺乏比较分析。因此，本研究分析了模拟微观行人和疏散动态的机器学习方法。提出了基于学习的模型的运动交互场以及一种标准化输入长度的数据提取规则。采用分类回归树（CART）和人工神经网络（ANN）两种典型算法进行模型训练和比较。使用速度的平均绝对误差来评估拟合性能，表明基于cart的模型在稳定性和更低的错误率方面优于基于ann的模型，特别是在不同的局部密度范围内。进一步进行了动力学测试，以检验两个模型对固有误差的鲁棒性。结果表明，由于基于分裂的结构，基于cart的模型在高密度条件下会遇到困难。相比之下，基于人工神经网络的模型表现出优越的非线性拟合能力，可以在相对较高的密度下更好地再现行人动态。此外，使用带有Sinkhorn迭代的Wasserstein距离来根据流量密度基本图量化模型性能，突出了基于学习的方法相对于传统社会力模型的优势。本研究对建筑和土木工程领域具有重要意义，通过对两种典型机器学习算法的比较分析和运动交互场的建立，可以为基于学习的行人和疏散动力学仿真的进展提供信息。该研究强调了机器学习方法在模拟行人动力学方面的变革潜力，并提出了未来的研究方向，以增强基于学习的方法在微观行人和疏散动力学模拟中不同场景的鲁棒性和适用性。

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引用次数: 0

Study on the evolution mechanism of three-dimensional fracture networks in rock induced by CO2 fracturing tube blasting CO2管爆致岩体三维裂隙网络演化机制研究

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-29 DOI: 10.1016/j.simpat.2025.103191

Zhiyuan Zhang , Weimin Yang , Meixia Wang , Linkun Jin , Xuan Song , Enming Zhang , Cong Tian , Fengqiang Gong

Carbon dioxide (CO₂) fracturing tubes have been applied as a novel blasting technique in rock blasting. However, the three-dimensional evolution of fracture networks induced by CO₂ blasting remains poorly investigated. Therefore, this study conducted on-site blasting tests on 1 m³ rock specimens. Field data were used to validate numerical simulations, and phase-transition blasting processes were further simulated under varying expansion ratios and loading durations. The results indicated a fractal dimension of 1.578 for the fracture network, with rock fragments exhibiting greater uniformity than those generated by traditional explosive blasting. The internal fracture network comprised interconnected radial and circumferential fracture planes. A linear positive correlation was observed among the particle expansion ratio, the total fracture count, and the input energy. Moreover, the density of radial fracture planes and the fracture network increased with the expansion ratio. In contrast, the total number of fractures and blasting energy demonstrated a quadratic inverse relationship with loading duration. Shorter loading durations led to a dense distribution of fracture networks around the blasting hole and increased heterogeneity of rock fragments. As the loading duration increases, the fracture number curve exhibited a significant lag compared to the particle expansion curve. These findings advance the mechanistic understanding of CO₂ fracturing tubes and optimize blasting efficiency.

二氧化碳（CO2）压裂管作为一种新型爆破技术在岩石爆破中得到了应用。然而，对CO2爆破引起的裂隙网络的三维演化研究仍然很少。因此，本研究对1 m3岩石试件进行了现场爆破试验。利用现场数据对数值模拟进行验证，并进一步模拟了不同膨胀比和加载时间下的相变爆破过程。结果表明，该裂缝网络的分形维数为1.578，岩石碎片比传统炸药爆破产生的均匀性更好。内部裂缝网络由相互连接的径向和周向裂缝面组成。颗粒膨胀率、总断裂数与输入能量呈线性正相关。径向裂缝面密度和裂缝网络密度随膨胀比增大而增大。裂缝总数和爆破能量与加载时间呈二次反比关系。较短的加载时间导致爆破孔周围裂隙网络分布密集，岩屑的非均质性增加。随着加载时间的增加，断裂数曲线与颗粒膨胀曲线相比表现出明显的滞后性。这些发现促进了对CO2压裂管的机理认识，优化了爆破效率。

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引用次数: 0

Multi-intersection platoon ecological speed planning strategy and method for autonomous driving simulation testing 自动驾驶仿真测试中多交叉口排生态速度规划策略与方法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-10 DOI: 10.1016/j.simpat.2025.103166

Chuanxiang Ren , Li Lu , Xiang Liu , Fangfang Fu , Lin Cheng

With the rapid development of Internet of Vehicles (IoV) technology, ecological speed planning has become a critical challenge in eco-driving, particularly in reducing energy consumption and improving the efficiency of autonomous vehicles. A key research focus is how to achieve energy savings and emission reductions by optimizing driving speed under various complex conditions, while simultaneously ensuring driving comfort and traffic efficiency. In view of this, a multi-intersection ecological speed planning strategy and method for autonomous platoon is proposed, aiming to reduce speed fluctuations and energy consumption of autonomous platoon in multiple driving scenarios. Firstly, the scenarios of platoon passing through the current intersection and its downstream intersection are analyzed, and then, the strategies for the platoon to pass through the current and its downstream intersections are proposed, including constant speed strategy (CSS) and segmented speed strategy (SSS). Moreover, the platoon ecological speed planning method is presented, which includes the calculation of the passage period, the capacity in the passage period of the intersections, and the platoon ecological speed. Finally, different simulation situations are designed in view of different ecological speed strategies, and compared with the single intersection platoon speed strategy (SIPSS) and the no speed strategy (NSS). The results indicate that the CSS and the SSS can mitigate the speed fluctuations of the platoon through intersections, reduce the fuel consumption and delay time, and outperform the SIPSS and NSS. Especially in the current intersection with a queuing platoon, the proposed strategy reduces fuel consumption and delay time by up to 67.21 % and 2.74 %, respectively.

随着车联网技术的快速发展，生态速度规划已成为生态驾驶的关键挑战，特别是在降低能源消耗和提高自动驾驶汽车效率方面。如何在各种复杂条件下通过优化行驶速度实现节能减排，同时保证驾驶舒适性和交通效率是一个关键的研究重点。鉴于此，提出了一种多交叉口的自主队列生态速度规划策略和方法，旨在减少自主队列在多驾驶场景下的速度波动和能量消耗。首先分析了车队通过当前交叉口及其下游交叉口的场景，然后提出了车队通过当前交叉口及其下游交叉口的策略，包括恒速策略（CSS）和分段速度策略（SSS）。在此基础上，提出了队列生态速度规划方法，包括交叉口通行周期、通行周期通行能力和队列生态速度的计算。最后，针对不同的生态速度策略设计了不同的仿真场景，并与单交叉口排速策略（SIPSS）和无速度策略（NSS）进行了比较。结果表明，CSS和SSS能有效缓解车辆通过交叉口时的速度波动，降低车辆的油耗和延迟时间，优于sips和NSS。特别是在当前存在排队队列的交叉口，该策略可将燃油消耗和延误时间分别降低67.21%和2.74%。

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引用次数: 0

Exploring the performance of real-time data imputation to enhance fault tolerance on the edge: A study on environmental data 基于边缘容错的实时数据输入性能研究——以环境数据为例

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-12 DOI: 10.1016/j.simpat.2025.103178

Dimitris Gkoulis, Anargyros Tsadimas, George Kousiouris, Cleopatra Bardaki, Mara Nikolaidou

Real-time data streams from edge-based IoT sensors are frequently affected by transmission errors, sensor faults, and network disruptions, leading to missing or incomplete data. This paper investigates the application of lightweight, real-time imputation methods to enhance fault tolerance in edge computing systems. To this end, we propose to integrate a modular imputation engine on edge system supporting lightweight forecasting models selected for their computational efficiency and suitability to operate on real-time data streams. To assess the performance of different popular lightweight forecasting models for real-time applications, a simulation framework is introduced that simulates the operation of the imputation engine, replicates sensor failure scenarios and allows controlled testing on real-world systems. Imputation accuracy is evaluated using Mean Absolute Error (MAE), 95th percentile error, and maximum error, with results benchmarked against sensor tolerance thresholds. The simulation framework is used to explore imputation on environmental data based on observations collected from a weather station. The findings show that Holt–Winters Exponential Smoothing delivers the highest accuracy for real-time imputation across environmental variables, outperforming simpler models suited only for short-term gaps. Errors grow with longer forecasts, confirming imputation as a temporary solution. Evaluations against sensor-specific thresholds offer practical insights, and execution profiling proves these models are lightweight enough for deployment on low-power edge devices, enabling real-time, fault-tolerant monitoring without cloud dependence.

来自边缘物联网传感器的实时数据流经常受到传输错误、传感器故障和网络中断的影响，导致数据丢失或不完整。本文研究了在边缘计算系统中应用轻量、实时的插值方法来增强容错性。为此，我们建议在边缘系统上集成一个模块化的输入引擎，支持轻量级的预测模型，这些预测模型是根据其计算效率和对实时数据流的适用性而选择的。为了评估实时应用中不同流行的轻量级预测模型的性能，引入了一个仿真框架，该框架模拟了输入引擎的操作，复制了传感器故障场景，并允许在真实系统上进行受控测试。使用平均绝对误差（MAE）、第95百分位误差和最大误差来评估插入精度，结果以传感器公差阈值为基准。利用模拟框架探讨了基于气象站观测数据的环境数据的拟合。研究结果表明，Holt-Winters指数平滑在跨环境变量的实时输入中提供了最高的准确性，优于仅适用于短期差距的简单模型。随着预测时间的延长，错误也会增加，这证实了归咎只是一种临时解决方案。针对特定传感器阈值的评估提供了实用的见解，执行分析证明这些模型足够轻量级，可以部署在低功耗边缘设备上，实现实时、容错监控，而不依赖云。

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引用次数: 0

CADCO: An Adaptive Dynamic Cloud-fog Computing Offloading Method for complex dependency tasks of IoT CADCO：物联网复杂依赖任务的自适应动态云雾计算卸载方法

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-03 DOI: 10.1016/j.simpat.2025.103168

Zhuangzhi Tian , Xiaolong Xu

With the rapid development of the Internet of Things (IoT) and cloud-fog computing, efficient offloading of complex dependency tasks has become a key challenge for improving system performance, especially for real-time IoT applications. Traditional methods are inefficient in handling dynamic environments and long-range dependencies, while existing deep reinforcement learning approaches face issues such as rigid resource allocation and Q-value overestimation. To address these problems, we propose an Adaptive Dynamic Cloud-fog Computing Offloading Method for complex dependency tasks (CADCO). The method accurately models task dependencies using the multi-head attention mechanism of Transformer, optimizes computational and memory resource allocation through Hybrid Model Parallelism (HMP) technology, and designs a dynamic offloading strategy based on an improved Double Deep Q-Network (DDQN). A freshness factor is introduced to optimize the experience replay mechanism, enhancing the stability of the strategy. Experimental results show that CADCO demonstrates significant advantages in multi-user, multi-task offloading scenarios, optimizing task scheduling, improving resource utilization, and significantly enhancing QoS while reducing task latency and energy consumption. These results validate the practical application value of CADCO in complex task dependency environments, providing solid theoretical and experimental support for intelligent computing offloading optimization.

随着物联网（IoT）和云雾计算的快速发展，复杂依赖任务的高效卸载已成为提高系统性能的关键挑战，特别是对于实时物联网应用。传统方法在处理动态环境和远程依赖关系方面效率低下，而现有的深度强化学习方法面临资源分配僵化和q值高估等问题。为了解决这些问题，我们提出了一种复杂依赖任务的自适应动态云雾计算卸载方法（CADCO）。该方法利用Transformer的多头注意机制精确建模任务依赖关系，通过混合模型并行（HMP）技术优化计算和内存资源分配，并设计了基于改进双深度Q-Network （DDQN）的动态卸载策略。引入新鲜度因子对体验回放机制进行优化，增强了策略的稳定性。实验结果表明，CADCO在多用户、多任务卸载场景下具有显著优势，可以优化任务调度，提高资源利用率，在降低任务延迟和能耗的同时显著增强QoS。这些结果验证了CADCO在复杂任务依赖环境中的实际应用价值，为智能计算卸载优化提供了坚实的理论和实验支持。

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引用次数: 0

Modeling the asymmetric thermo-mechanical behavior and failure of gray cast irons: An experimental–numerical study with separate Johnson–Cook parameters 模拟灰铸铁的不对称热力学行为和失效：具有单独Johnson-Cook参数的实验-数值研究

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-07-14 DOI: 10.1016/j.simpat.2025.103182

Burak Özcan , Umut Çalışkan , Murat Aydın , Onur Çavuşoğlu , Ulvi Şeker

In this study, the asymmetric (different tensile and compressive behavior) thermo-mechanical behavior and damage of gray cast irons (EN-GJL-200, EN-GJL-250, EN-GJL-300), which are widely used in industrial applications, under different strain rates and temperatures were investigated by a combination of experimental and numerical methods. The mechanical response of the materials was characterized by quasi-static tensile and compression tests at room temperature and elevated temperatures up to 700 °C, Split Hopkinson Compression Bar (SHPB) tests for high strain rates (up to ∼3600 s⁻¹) and tensile tests with specimens of different notch radii to analyze the damage behavior. Based on the experimental data obtained, the Johnson-Cook (JC) material (A, B, n, C, m) and damage (D1-D5) model parameters were calibrated separately for both loading cases in order to capture the apparent asymmetric behavior of gray cast irons under tensile and compression loading. These separate parameter sets were integrated into ANSYS Autodyn finite element software through FORTRAN-based user-defined subroutines and virtual tensile, compression and SHPB tests were performed. Comparing the numerical simulation results with the experimental data, it was observed that the developed asymmetric modeling approach, in particular, represents the thermo-mechanical behavior and damage of the material with high accuracy (deviations in the range of 2–8 % for maximum stress and elongation at break values). This study provides reliable and decoupled JC parameter sets for modeling the asymmetric thermo-mechanical behavior and damage of gray cast irons, allowing more realistic simulations to predict the performance of these materials in demanding engineering applications.

采用实验与数值相结合的方法，研究了工业上广泛应用的灰口铸铁（EN-GJL-200、EN-GJL-250、EN-GJL-300）在不同应变速率和温度下的不对称（不同的拉伸和压缩行为）热力学行为和损伤。材料的力学响应通过室温和高达700°C的高温下的准静态拉伸和压缩试验、高应变率（高达~ 3600 s−1）的劈裂霍普金森压缩杆（SHPB）试验和不同缺口半径试样的拉伸试验来分析损伤行为。基于获得的实验数据，分别校准了两种加载情况下的Johnson-Cook （JC）材料（A， B, n， C, m）和损伤（D1-D5）模型参数，以捕捉灰口铸铁在拉伸和压缩加载下的明显不对称行为。通过基于fortran的用户自定义子程序将这些单独的参数集集成到ANSYS Autodyn有限元软件中，并进行虚拟拉伸、压缩和SHPB试验。将数值模拟结果与实验数据进行比较，发现所开发的非对称建模方法能够高精度地表征材料的热力学行为和损伤（最大应力和断裂伸长率的偏差在2 - 8%范围内）。该研究为灰口铸铁的不对称热力学行为和损伤建模提供了可靠且解耦的JC参数集，允许更真实的模拟来预测这些材料在苛刻的工程应用中的性能。

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引用次数: 0

A flexible hybrid simulation model for hospital capacity management through multimodal transfers of COVID-19 patients 基于COVID-19患者多式联运的医院容量管理灵活混合仿真模型

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-08-06 DOI: 10.1016/j.simpat.2025.103192

Sven Watzinger , David Olave-Rojas , Janina Bathe , Hanna-Joy Renner , Jan Wnent , Leonie Hannappel , Jan-Thorsten Gräsner , Stefan Nickel

The global pandemic provoked by the SARS-CoV-2 virus in recent years has presented new challenges to health care systems. One major issue is the risk of overloading hospital capacities during regional surges, especially in intensive care units. Strategic patient transfers between regions with different loads can mitigate this risk. To coordinate such nationwide strategic patient transfers in Germany, the clover-leaf system was initiated. The transfer decision consists of allocating patients to destination hospitals as well as scheduling patients on transport vehicles which includes the possibility of combining different modes of transport, for instance ground-based with an ambulance and air-based with a helicopter, during one transfer. As potentially conflicting objective dimensions the impact of the transfers on the transferred patients and the impact on loads in intensive care units have to be considered. To support the decision makers a hybrid simulation model combining agent-based and discrete-event modeling is developed by an interdisciplinary team of medical and operations research experts. The main contribution of the simulation model is the modeling of multimodal patient transfers which to the best of our knowledge has not been considered in the existing literature. Next to the simulation model, several transfer strategies in the form of decision rules are proposed. These transfer strategies are used to benchmark transfer plans created by the decision makers in a test scenario based on nationwide data of the German health care system. Using simulation allowed to evaluate the transfer plans in different objective dimensions and informed the decision-making process.

近年来，由SARS-CoV-2病毒引发的全球大流行给卫生保健系统带来了新的挑战。一个主要问题是，在区域激增期间，医院的能力可能会超载，特别是在重症监护病房。有策略地在不同负荷地区之间转移病人可以减轻这种风险。为了在德国协调这种全国性的战略性病人转移，三叶草系统被启动。转移决定包括将病人分配到目的地医院以及安排病人乘坐运输车辆，其中包括在一次转移期间结合不同运输方式的可能性，例如地面与救护车和空中与直升机。作为潜在冲突的客观维度，转移对转移患者的影响和对重症监护病房负荷的影响必须加以考虑。为了支持决策者，由医学和运筹学专家组成的跨学科团队开发了基于智能体和离散事件建模相结合的混合仿真模型。模拟模型的主要贡献是多模式患者转移的建模，据我们所知，在现有文献中尚未考虑到这一点。在仿真模型的基础上，以决策规则的形式提出了几种迁移策略。这些转移策略用于在基于德国医疗保健系统全国数据的测试场景中对决策者制定的转移计划进行基准测试。通过仿真可以在不同的客观维度上对迁移方案进行评估，为决策过程提供信息。

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引用次数: 0

A novel open-source framework for performing TSN schedules 一个用于执行TSN调度的新颖的开源框架

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory

Pub Date : 2025-11-01 Epub Date: 2025-06-06 DOI: 10.1016/j.simpat.2025.103147

Aellison C.T. Santos , Renan M. Silva , Ben Schneider , Malte Wilhelm , Iguatemi E. Fonseca , Vivek Nigam

Due to the complexity of deployed networks, as well as its NP-complete traffic scheduling problem (Craciunas et al., 2016b), Time Sensitive Networking (TSN) configuration is error-prone and challenging when done manually. We present TSNsched, an open-source framework for TSN configuration. The proposed framework has workflows that enable the generation, validation, and deployment of TSN schedules. TSNsched takes as input the network logical topology, expressed as flows, its latency and jitter requirements, generating schedules for TSN switches by reducing different variations of traffic scheduling problems to logical theories that can be automatically solved using Satisfiability Modulo Theories (SMT) solvers. TSNsched provides customized network simulators for validation of the generated schedules. We describe by example how these tool workflows can be used to analyze, validate, and deploy TSN configurations.

由于部署网络的复杂性，以及其NP-complete流量调度问题（Craciunas et al., 2016b），时间敏感网络（TSN）配置在手动完成时容易出错且具有挑战性。我们提出TSNsched，一个用于TSN配置的开源框架。建议的框架具有支持TSN计划的生成、验证和部署的工作流。TSNsched将以流表示的网络逻辑拓扑、时延和抖动需求作为输入，通过将流量调度问题的不同变化形式简化为可以使用可满足模理论（Satisfiability Modulo theories， SMT）求解器自动求解的逻辑理论，生成TSN交换机的调度。TSNsched提供定制的网络模拟器来验证生成的时间表。我们通过示例描述如何使用这些工具工作流来分析、验证和部署TSN配置。

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