Simulation Modelling Practice and Theory最新文献

{"title":"Active learning confidence measures for coupling strategies in digital twins integrating simulation and data-driven submodels","authors":"Sylvain Chabanet,&nbsp;Hind Bril El-Haouzi,&nbsp;Philippe Thomas","doi":"10.1016/j.simpat.2025.103092","DOIUrl":"10.1016/j.simpat.2025.103092","url":null,"abstract":"<div><div>Many challenges have been raised in the scientific literature regarding the development of digital twins that can predict future states of production processes from data streams. This study is concerned with the coordination of several of their submodels to balance precision with computational requirements. A method to use stream-based active learning sampling strategies to couple two such models is proposed. Both models perform the same prediction task but have different advantages and disadvantages. The first is a simulation model that is supposed to have a high fidelity level, but to be slow. The second is a machine learning model, which is fast but less accurate and requires many labeled examples to be trained on, which may require a lot of time and effort to gather. The objective is to leverage confidence measures in the predictions of the machine learning model. These measures are used to couple the two models and take advantage of their respective strengths. In particular, the aim is to reduce the digital twin’s average prediction error while operating under limited computational capacity. Moreover, an application within the sawmill industry and numerical experiments are presented.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103092"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D vision object detection for autonomous driving in fog using LiDaR","authors":"Alishba Tahir,&nbsp;Rafia Mumtaz,&nbsp;Muhammad Saqib Irshad","doi":"10.1016/j.simpat.2025.103089","DOIUrl":"10.1016/j.simpat.2025.103089","url":null,"abstract":"<div><div>Connected and Autonomous Vehicles (CAVs) are transforming transportation. The paper describes a new method of fog simulation applied to LiDAR data for self-driving cars with a focus on enhancing 3D object detection in low visibility conditions. As opposed to the previously used methods, synthetic fog augmentation is combined with deep learning models and it is proven that the proposed method is superior to the previous methods when it comes to object detection accuracy in various fog levels. Another challenge that has been discussed in the study to ensure the reliability of autonomous navigation is the question of how the fog and the LiDAR point cloud should be modeled which eventually helps in improving the decision-making safety and operation. Fog can drastically reduce visibility and safety, making it crucial to test LiDAR-based perception algorithms for CAVs under such conditions. These simulations aim to ensure CAVs can navigate safely and efficiently through fog. However, challenges like sensor calibration and data integration need to be addressed. Despite these hurdles, the research foresees a future where CAVs, equipped with advanced LiDAR-based perception algorithms and fog-handling capabilities, enhance safety and efficiency in transportation. Notably, using synthetic fog augmentation improved detection by 5.27% for cars and 8.11% for cyclists. Furthermore, the study showcases improvements of 4.76%, 2.92%, and 3% in Mean Average Precision (mAP) across the distinct object categories of easy, moderate, and hard difficulty levels, respectively.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103089"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computation offloading and task caching in the cloud–edge collaborative IoVs: A multi-objective evolutionary algorithm","authors":"Zi-xin Chai ,&nbsp;Zheng-yi Chai ,&nbsp;Junjun Ren ,&nbsp;Dong Yuan","doi":"10.1016/j.simpat.2025.103087","DOIUrl":"10.1016/j.simpat.2025.103087","url":null,"abstract":"<div><div>With rapid development of Internet of Vehicles (IoVs), various computation-intensive vehicular applications impose great challenges on the limited computing resources of vehicles. To improve the user experience of vehicular applications, the emerging vehicular edge computing (VEC) offloads tasks to roadside edge servers. However, competition over communication and computing resources is inevitable among vehicles. How to make optimal task offloading decisions for vehicles, so as to reduce delay, balance server load and save energy, is worth researching in-depth. In this paper, first, a vehicle-to-vehicle (V2V) communication path acquisition algorithm is designed, and a task caching mechanism introduced which cache some completed applications and related codes on the edge server. Then, a vehicular networking model with joint task caching mechanism for edge–cloud collaboration is proposed. To obtain the near-optimal solutions to this problem, we design a multi-objective evolutionary algorithm based joint task caching and edge–cloud computing decision algorithm (JTCEC-MOEA/D) to maximize the utilities of vehicles. Finally, the proposed algorithm is evaluated by the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The simulation results show that the proposed algorithm can make optimal task offloading-making for vehicles.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"141 ","pages":"Article 103087"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging machine learning and feature engineering for optimal data-driven scaling decision in serverless computing","authors":"Mustafa Daraghmeh ,&nbsp;Yaser Jararweh ,&nbsp;Anjali Agarwal","doi":"10.1016/j.simpat.2025.103090","DOIUrl":"10.1016/j.simpat.2025.103090","url":null,"abstract":"<div><div>Serverless computing offers scalability and cost-efficiency, but balancing performance and cost remains challenging, particularly in scaling decisions that can lead to cold starts or resource misallocation. This research is motivated by the need to minimize the impact of cold starts and optimize resource utilization in serverless applications by developing intelligent, data-driven scaling decisions. We delve into using machine learning and feature engineering to model and simulate predictions for optimal scaling decisions for Azure Function Apps (AFA). Our focus lies in predicting the ideal timing for provisioning or de-provisioning the Function App’s environment. Using historical invocation data, we applied a sliding window to transform the time-series data into patterns categorized as load or unload classes, considering various target periods. To identify the most effective model, we compared the performance of various baseline models with and without calibration (isotonic and sigmoid) to enhance precision. In addition, we assess multiple feature extraction methods in invocation patterns and explore the use of Principal Component Analysis (PCA) for dimensionality reduction to reduce computation costs. Using the best-identified configurations, we model and simulate the class patterns over time to compare the actual classes with the predicted ones, focusing on memory usage and the costs associated with cold starts. The proposed model is thoroughly evaluated using various metrics under different setups, revealing notable improvements in scaling decisions achieved by applying calibration and feature engineering methods. These findings demonstrate the potential of machine learning for intelligent, data-driven scaling decisions in serverless computing, offering valuable insights for cloud providers to optimize resource allocation and for developers to build more efficient and responsive serverless applications. Specifically, the proposed method can be integrated into serverless platforms to automatically adjust resource provisioning based on predicted workload demands, reducing cold start latency and improving cost-effectiveness.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103090"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical prediction of residual stress and distortion for laser powder bed fusion (LPBF) AM process of Ti-6Al-4V","authors":"J. Mohanraj,&nbsp;Jambeswar Sahu","doi":"10.1016/j.simpat.2025.103094","DOIUrl":"10.1016/j.simpat.2025.103094","url":null,"abstract":"<div><div>The demand of additive manufacturing (AM) processes has increased in the industry due to its realistic printing of complex geometry parts. The process involves with continuous melting of powder and rapid solidification. The heating and cooling attributes to the formation of residual stress which leads to the distortion in the AM part. The prediction of distortion and residual stress before printing could minimize the rejection of parts due to dimensional variation. In the present research work, an attempt was made to simulate LPBF AM process using MSC Simufact software for Ti-6Al-4V material. The simulation results are compared with the existing literature. The simulation parameters are optimized to minimize the deviation between experimental and simulation results. The inherent strain value, voxel size and other simulation parameters are utilized to predict the distortion and residual stress of a micro-tensile specimen. The distortion and residual are predicted in different orientations (0°, 30°, 45°, 60° and 90°) and at position of the base plate. It is observed that voxel size (accumulation of physical layers) has a significant effect on the prediction accuracy. The specimen placed near the power collector bin and gas inlet side shows minimum residual stress. The residual stress in the gauge section of 45° orientation is minimal compared to other-oriented specimens. The limited distortion is noticed for the 0° orientation specimen as the height of the sample is minimal.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103094"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BRAVE: Benefit-aware data offloading in UAV edge computing using multi-agent reinforcement learning","authors":"Odyssefs Diamantopoulos Pantaleon,&nbsp;Aisha B Rahman,&nbsp;Eirini Eleni Tsiropoulou","doi":"10.1016/j.simpat.2025.103091","DOIUrl":"10.1016/j.simpat.2025.103091","url":null,"abstract":"<div><div>Edge computing has emerged as a transformative technology in public safety and has the potential to support the rapid data processing and real-time decision-making during critical events. This paper introduces the BRAVE framework, a cutting-edge solution where the UAVs act as Mobile Edge Computing (MEC) servers, addressing users’ computational demands across disaster-stricken areas. An accurate UAV energy consumption model is introduced, including the UAV’s travel, processing, and hover energy. BRAVE accounts for both the users’ Quality of Service (QoS) requirements, such as latency and energy constraints, and UAV energy limitations in order to determine the UAVs’ optimal path planning. The BRAVE framework consists of a two-level decision-making mechanism: a submodular game-based model ensuring the users’ optimal data offloading strategies, with provable Pure Nash Equilibrium properties, and a reinforcement learning-driven UAV path planning mechanism maximizing the data collection efficiency. Furthermore, the framework extends to collaborative multi-agent reinforcement learning (BRAVE-MARL), enabling the UAVs’ coordination for enhanced service delivery. Extensive experiments validate the BRAVE framework’s adaptability and effectiveness and provide tailored solutions for diverse public safety scenarios.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103091"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mesoscopic link-transmission-model able to track individual vehicles","authors":"Felipe de Souza ,&nbsp;Omer Verbas ,&nbsp;Joshua Auld ,&nbsp;Chris M.J. Tampère","doi":"10.1016/j.simpat.2025.103088","DOIUrl":"10.1016/j.simpat.2025.103088","url":null,"abstract":"<div><div>Macroscopic traffic flow is a common choice for large-scale traffic simulations. These models do not provide individual-specific metrics as outputs. However, this treatment is necessary in agent-based-models, as in, for example, assigning routes based on personal characteristics. In this paper, we propose an extension of the link-transmission-model, an efficient and yet accurate discretization of the Lighthill–Whitham–Richards (LWR) model, which allow vehicles to be tracked individually while keeping the main features of the underlying model. The extension comprises modifying the link and node models to ensure that the flow between links is always at discrete levels. Therefore, every unit of flow is associated with one individual vehicle moving from its current to its next link. An upper bound of the discretization error is provided. We show that the proposed model resembles its continuous counterpart on lane drop, merge, and diverge cases. In addition, we apply the model into three different networks to validate its applicability in large networks. Finally, we also confirm the parameter transferability between continuous and discrete models and that both can well reproduce field data.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103088"},"PeriodicalIF":3.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of communication performance measures for the Internet of Vehicles in an intersection scenario","authors":"Haijian Li ,&nbsp;Qi Zhang ,&nbsp;Lijun Wu ,&nbsp;Zihan Zhang","doi":"10.1016/j.simpat.2025.103086","DOIUrl":"10.1016/j.simpat.2025.103086","url":null,"abstract":"<div><div>With the development of the Internet of Vehicles (IoV) technology and the advancement of autonomous driving technology, the efficiency of traffic flow has improved and the safety of driving and the convenience of travel have becoming better and better. However, in real-world intersection scenarios, there are still circumstances where the delay between vehicles is increased and the communication distance is largely decreased. Many scholars worldwide have analyzed and discussed the performance of LTE Mode 4 and the development of autonomous driving, such as the application and design of the LTE Mode 4. Therefore, this paper constructs a virtual IoV communication environment and conducts research on intersection control methods under the influence of communication performance. This paper provides reference values for the stability of vehicle communication network in actual road traffic pattern and provides a theoretical basis and data support for subsequent research on autonomous and adaptive sensor-based intersections.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103086"},"PeriodicalIF":3.5,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing coloured Petri nets to enhance machine learning:A simulation-based method for healthcare and beyond","authors":"Andressa C.M. da Silveira ,&nbsp;Álvaro Sobrinho ,&nbsp;Leandro Dias da Silva ,&nbsp;Danilo F.S. Santos ,&nbsp;Muhammad Nauman ,&nbsp;Angelo Perkusich","doi":"10.1016/j.simpat.2025.103080","DOIUrl":"10.1016/j.simpat.2025.103080","url":null,"abstract":"<div><div>Many industries use Machine Learning (ML) techniques to enhance systems’ performance. However, integrating ML into these systems poses challenges, often requiring improved explainability and accuracy. Using formal methods is a potential solution to address these challenges. This paper presents a simulation-based method using Coloured Petri Nets (CPN) to enhance the explainability and accuracy of Decision Tree (DT) and Random Forest (RF) models, which industries such as healthcare widely adopt. Our simulation-based method, named RuleXtract/CPN, provides procedures for the automatic extraction of decision rules from an implemented ML model, the generation of these decision rules into a CPN model, the analysis of the CPN model through simulations, and the adjustment of the CPN model to improve explainability and accuracy. Automating the transformation from DT/RF to a CPN model and the analysis procedures can reduce the time and effort needed for modeling tasks. We used web technologies and the Access/CPN framework to implement the procedures defined in our simulation-based method so that users would not need CPN expertise to generate and simulate models, running them in the background. An experiment with three datasets for COVID-19 and five for Influenza screening shows that applying our simulation-based method results in more explainable models. The experiment also shows improvement in accuracy measures for RF models. For instance, the accuracy of the RF model using the Influenza rapid test balanced dataset increased from 84.02% to 86.34%, and the unbalanced dataset from 84.78% to 87.53%. Our results underscore the importance of eliminating duplicated, poorly generalized, and incorrect rules to improve explainability and accuracy. These findings also emphasize the effectiveness of using CPN to improve the models, paving the way for future research.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103080"},"PeriodicalIF":3.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient congestion control scheme for railway transport networks","authors":"Zongtao Duan,&nbsp;Jianrong Cao,&nbsp;Xing Sheng,&nbsp;Junzhe Zhang","doi":"10.1016/j.simpat.2025.103085","DOIUrl":"10.1016/j.simpat.2025.103085","url":null,"abstract":"<div><div>As the complexity of railway transmission network services continues to increase, burst traffic and the mixing of signaling have become significant challenges in congestion control. This paper presents a congestion control strategy based on the stochastic flow queue-controlled delay (SFQ-CoDel) algorithm, developed through an analysis of the traffic characteristics and operational demands of contemporary railway transmission networks. The scheme primarily integrates a random flow queue mechanism with a dynamic Hurst coefficient calculation method. The random flow queue employs hash mapping to distinguish data packets, thereby ensuring fair bandwidth allocation across active sub-flows. The dynamic computation of the Hurst coefficient, coupled with a minimum queue delay, formulates a packet loss strategy that effectively mitigates the effects of burst traffic. Experimental results indicate that the SFQ-CoDel algorithm excels in minimizing packet loss, enhancing throughput, and maintaining stable queue lengths, regardless of the load. Additionally, an analysis of parameter adjustability confirms that, even with the inclusion of the stochastic flow queue (SFQ) mechanism, the CoDel parameters consistently sustain optimal algorithm performance. Therefore, the proposed congestion control scheme provides a robust and adaptable framework for managing congestion within railway transmission networks.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"140 ","pages":"Article 103085"},"PeriodicalIF":3.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}