Simulation Modelling Practice and Theory最新文献

{"title":"Intelligent dynamic bandwidth allocation for real-time IoT in fog-based optical networks","authors":"Mohannad Alhafnawi ,&nbsp;Ashraf Abu-Ein ,&nbsp;Haythem Bany Salameh ,&nbsp;Yaser Jararweh ,&nbsp;Obaida Al-Hazaimeh","doi":"10.1016/j.simpat.2025.103126","DOIUrl":"10.1016/j.simpat.2025.103126","url":null,"abstract":"<div><div>The rapid proliferation of Internet of Things (IoT) applications has led to an exponential increase in data traffic, which is imposing a significant burden on existing network infrastructures. Ethernet Passive Optical Networks (EPONs) within fog computing environments are critical for supporting real-time IoT applications because of their high bandwidth and low-latency capabilities. However, traditional static bandwidth allocation methods designed for EPONs do not accommodate the dynamic and heterogeneous nature of real-time IoT traffic, leading to inefficient resource utilization and service degradation. This paper proposes a reinforcement learning (RL)-driven fog-based dynamic bandwidth allocation (DBA) framework to optimize resource management in EPONs with multi-optical edge devices (OEDs) within cloud-fog computing environments. The framework improves bandwidth distribution for OEDs connected to IoT access points through a fog server, ensuring adaptive real-time dynamic bandwidth allocation. Specifically, this paper formulates the bandwidth allocation problem as a constrained profit-maximization discounted return problem, which cannot be directly solved using traditional optimization methods due to the dynamic and uncertain nature of the IoT traffic. To effectively solve this problem, this paper models the bandwidth allocation problem as a Markov Decision Process (MDP), which can be solved using RL without requiring prior traffic knowledge. Through a continuous real-time learning process, the proposed algorithm learns the IoT traffic dynamics and then adaptively optimizes bandwidth allocation at the fog layer, resulting in improved system efficiency and adaptability. Simulation results demonstrate that, compared to conventional bandwidth allocation algorithms designed for fog-based EPONs (i.e., equal distribution and moving average algorithms), the proposed RL-based framework significantly improves bandwidth utilization and reduces blocking probability related to IoT networks by dynamically adjusting bandwidth allocation based on the learned real-time traffic.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103126"},"PeriodicalIF":3.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886547","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":"Topology Rewiring Strategies to construct robust scale-free medical Internet of Things Networks","authors":"Muhammad Awais Khan ,&nbsp;Nadeem Javaid ,&nbsp;Nabil Alrajeh ,&nbsp;Safdar Hussain Bouk","doi":"10.1016/j.simpat.2025.103123","DOIUrl":"10.1016/j.simpat.2025.103123","url":null,"abstract":"<div><div>The Internet of Things (IoT) network topologies are now most commonly impacted by cyberattacks. The scale-free network topologies have demonstrated great robustness against random attacks by preserving the connectedness of the nodes. The scale-free network topologies’ susceptibility to malicious attacks, however, is a significant worry. It is due to the significance of the scale-free networks in different fields of life like medical, transportation, education, agriculture, etc. Also, high-degree node removal diminishes the network’s resiliency and compromises the connection of the majority of nodes. In this study, we offer several rewiring techniques for building scale-free, reliable Medical Internet of Things (MIoT) networks that can withstand malicious attacks. Initially, the scale-free MIoT network’s performance optimization is ensured using a heuristic algorithm known as the Great Deluge Algorithm (GDA). Then, four rewiring strategies are formulated. The initial approach is degree dissortativity, which rewires the network if all nodes have high maximum connectivity to other neighbors with a similar degree. For the second strategy, we introduced a degree difference operation based on degree dissortativity to ensure that the edges that are connected possess low dissortativity and degree difference. Meanwhile, the remaining two strategies take into account the node load bound and enhanced GDA to increase network robustness. The performance of the proposed rewiring strategies is validated through simulations. The results prove that the proposed strategies increase network robustness by up to 25% compared to Hill Climbing (HC) and Simulated Annealing (SA). Additionally, the strategies show great success in improving network connectivity and graph density. However, their computational time is higher compared to HC and SA.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103123"},"PeriodicalIF":3.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878835","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":"Insights into the metal cutting contact zone through automation and multivariate regression modelling under the framework of gear skiving","authors":"Florian Sauer ,&nbsp;Amartya Mukherjee ,&nbsp;Volker Schulze","doi":"10.1016/j.simpat.2025.103107","DOIUrl":"10.1016/j.simpat.2025.103107","url":null,"abstract":"<div><div>The modern time of Industry 4.0 requires an enhanced prediction process for reliable and sustainable manufacturing. It is essential to understand the relationships between various process parameters of machining for better optimization. Digitalization offers the opportunity to accelerate the prediction process using different modelling such as numerical and data-driven models. Improvements in the knowledge of thermo-mechanical variables and the use of finite element method (FEM) tools and machine learning approaches for thorough thermo-mechanical analysis are noteworthy contributions to the area. However, an ideal standardized approach remains to be resolved. Therefore, this research proposes a development process of an automated FEM tool to simulate the tool-chip interaction for AISI4140 material, coupled with a hybrid multivariate regression model for fast prediction of non-linear relationships between the cutting parameters and the contact properties. Consequently, the study also interprets the tool-chip interactions in the secondary deformation zone, facilitating process optimization for improved machining performance.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103107"},"PeriodicalIF":3.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863778","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 integrated framework for Multi-AMR based CL-CBS and MPC-APF in warehousing scenario","authors":"Xu Sun ,&nbsp;Ming Yue ,&nbsp;Heyang Wang ,&nbsp;Yang Liu ,&nbsp;Xudong Zhao","doi":"10.1016/j.simpat.2025.103122","DOIUrl":"10.1016/j.simpat.2025.103122","url":null,"abstract":"<div><div>Aiming at the problem of Multi-Autonomous mobile robots (Multi-AMR) performing autonomous handling tasks in warehouse scenario, this paper proposes a framework that combines the Car like-Conflict based search algorithm (CL-CBS) and the Model predictive control-Artificial potential field algorithm (MPC-APF) is proposed for local trajectory replanning and tracking control. First, the CL-CBS is employed at the global trajectory planning layer; the algorithm uses a binary tree-based conflict search algorithm at the top-level and a spatiotemporal Hybrid-A* algorithm at the lower-level, which allows Multi-AMR to plan collision-free trajectories in compliance with the Ackermann kinematic characteristics. Second, at the trajectory replanning layer, the quintic polynomial equation is employed to fit segments to the discrete points with temporal information to enhance the smoothness and feasibility of the trajectory. Then, an function is proposed which incorporates the features of the APF in the form of an obstacle avoidance function into the optimization solution of the MPC. Finally, at the trajectory tracking control layer, a leapfrog speed planning is proposed, and a dynamics model is used to perform tracking control on the trajectories input from the replanning layer. Moreover, a structured warehousing map is built on virtual environments to validate the framework, and the results verify its safety and feasibility.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103122"},"PeriodicalIF":3.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834187","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":"Optimization of Veno parameter based on stochastic approximation: OVeno","authors":"Subhra Priyadarshini Biswal,&nbsp;Sanjeev Patel","doi":"10.1016/j.simpat.2025.103121","DOIUrl":"10.1016/j.simpat.2025.103121","url":null,"abstract":"<div><div>Transmission Control Protocol (TCP) ensures reliable communication between source and destination. However, TCP’s performance is significantly affected by congestion control, which regulates data flow and maintains optimal transfer rates while preventing packet loss. Congestion control is managed by the router that is network-assisted, and another approach is controlled by TCP, which is end-to-end congestion control. A popular TCP congestion control algorithm, Veno has the advantage of distinguishing between random loss and congestion loss. Veno serves as the base algorithm of TCP, performing well even in the presence of a wireless environment. These important features motivate us to redesign the Veno. This paper proposes a modified multiplicative decrease phase of the TCP Veno algorithm based on the stochastic approximation that is used to determine the optimal value of parameters. The performance evaluation of the proposed algorithm is evaluated with recent existing algorithms. The experimental result shows that the proposed approach improves the performance of the existing standard algorithms in terms of loss rate, throughput, and delay. Our proposed algorithm improves throughput by 143%, 131%, 66%, and 42% compared to Reno, Compound TCP, CUBIC, and Veno, respectively. We have also tested the efficacy of our proposed algorithm in the wireless environment.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103121"},"PeriodicalIF":3.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828879","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 advantage duPLEX dueling multi-agent Q-learning algorithm for multi-UAV cooperative target search in unknown environments","authors":"Xiaoran Kong ,&nbsp;Jianyong Yang ,&nbsp;Xinghua Chai ,&nbsp;Yatong Zhou","doi":"10.1016/j.simpat.2025.103118","DOIUrl":"10.1016/j.simpat.2025.103118","url":null,"abstract":"<div><div>Multiple unmanned aerial vehicles (UAVs) cooperative target search has been extensively applied in post-disaster relief and surveillance tasks. However, achieving efficient cooperative target search in unknown environments without prior information is extremely challenging. In the study, a novel multi-agent deep reinforcement learning (MADRL)-based approach is proposed to enable UAVs to execute target search in the three-dimensional (3D) unknown environments. Specifically, the target search problem is formulated as a decentralized partially observable Markov decision processes (Dec-POMDP), where each UAV maintains its own target existence probability map and merges with those of other UAVs within communication range to enhance UAVs’ perception of task environment. Then, an improved duPLEX dueling multi-agent Q-learning (QPLEX) algorithm called Advantage QPLEX is proposed to make the optimal decision for multiple UAVs target search. The Advantage QPLEX can guide UAVs to focus on the advantage steps during the search to improve search efficiency, and direct UAVs to select the advantage action in each step for a greater return. In addition, a novel reward function is well-designed for cooperative target search problems to drive UAVs to explore and utilize the environmental information efficiently. Extensive simulations conducted on the Airsim validate that the Advantage QPLEX outperforms the existing algorithms in terms of the coverage rate and search rate.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103118"},"PeriodicalIF":3.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834188","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 iterative surrogate-based optimization approach for multi-server queuing system design","authors":"Carla Pineda,&nbsp;Alfredo Santana,&nbsp;Rafael Batres","doi":"10.1016/j.simpat.2025.103119","DOIUrl":"10.1016/j.simpat.2025.103119","url":null,"abstract":"<div><div>Queuing systems play an important role in numerous domains, including banks, supermarkets, traffic control, call centers, and production processes. Traditional methods for designing multi-server queuing systems often rely on trial-and-error or extensive simulations, making them time-consuming and computationally expensive. This paper addresses these challenges using MEVO (Metamodel-based Evolutionary Optimizer), a surrogate-based optimization algorithm. MEVO employs a machine-learning model as a surrogate model, reducing reliance on computationally intensive simulations. The algorithm also integrates evolutionary operators for efficient solution space exploration, a long-term memory strategy to avoid redundant simulations, and a dynamic search space reduction mechanism to enhance optimization efficiency.</div><div>A case study of a supermarket checkout system, modeled in FlexSim, demonstrates the algorithm’s efficacy in optimizing queuing configurations under stochastic variables such as customer arrival rates, basket sizes, and transaction values. MEVO achieves solution-quality performance comparable to the FlexSim optimizer while significantly reducing computation times. MEVO also delivers comparable computational performance to Bayesian optimization while exhibiting lower variance in objective-function results than FlexSim, highlighting its consistency and robustness.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103119"},"PeriodicalIF":3.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799921","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":"Robust optimization method for co-simulation of equipment based on EDEM-ADAMS","authors":"Jing Guo,&nbsp;Jin Qi,&nbsp;Jie Hu,&nbsp;Chengan Hong,&nbsp;Yuliang Shen,&nbsp;Haiqing Huang,&nbsp;Weijie Liu","doi":"10.1016/j.simpat.2025.103124","DOIUrl":"10.1016/j.simpat.2025.103124","url":null,"abstract":"<div><div>Tunneling machines, pivotal in rock tunnel excavation, utilize cutting mechanisms for rock fragmentation. As the core component of the cutting mechanism, the cutting head experiences severe vibrations during the rock breaking process when subjected to large loads, which adversely affects the working performance of the tunneling machines. The precision and efficiency of cutting force simulation for the cutting head are crucial for equipment design optimization and performance assessment. Therefore, exploring robust simulation time steps is particularly significant. This paper leverages state-of-the-art simulation techniques to boost the accuracy and computational performance of cutting head simulation. Firstly, by setting 44 different combinations of EDEM-ADAMS time steps, simulations are conducted in four different environments to collect cutting forces and simulation time data. Then, in view of this dataset, the radial basis function (RBF) approximation model is developed to simultaneously predict cutting forces and simulation time under four environments, which enhances the accuracy and applicability of the predictions. Finally, targeting the minimization of relative error, fluctuation magnitude, and simulation time, the NSGA-II algorithm is further utilized for multi-objective iterative optimization to obtain the time step combination with excellent performance. The results demonstrate that the optimized method reduces the relative error by 67.8 %, the fluctuation magnitude by 43.6 %, and the simulation time by 31.8 %. These improvements highlight the effectiveness of the optimization approach in enhancing both the precision of cutting force prediction and the stability of the simulation process, while maintaining computational efficiency.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103124"},"PeriodicalIF":3.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821532","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":"How periodic forecast updates influence MRP planning parameters: A simulation study","authors":"Wolfgang Seiringer ,&nbsp;Klaus Altendorfer ,&nbsp;Thomas Felberbauer ,&nbsp;Balwin Bokor ,&nbsp;Fabian Brockmann","doi":"10.1016/j.simpat.2025.103115","DOIUrl":"10.1016/j.simpat.2025.103115","url":null,"abstract":"<div><div>In many supply chains, the current efforts at digitalization have led to improved information exchanges between manufacturers and their customers. Specifically, demand forecasts are often provided by the customers and regularly updated as the related customer information improves. In this paper, we investigate the influence of forecast updates on the production planning method of Material Requirements Planning (MRP). A simulation study was carried out to assess how updates in information affect the setting of planning parameters in a rolling horizon MRP planned production system. An intuitive result is that information updates lead to disturbances in the production orders for the MRP standard, and, therefore, an extension for MRP to mitigate these effects is developed. A large numerical simulation experiment shows that the MRP safety stock exploitation heuristic, that has been developed, leads to significantly improved results as far as inventory and backorder costs are concerned. An interesting result is that the fixed-order-quantity lotsizing policy performs—in most instances—better than the fixed-order-period lotsizing policy, when periodic forecast updates occur. In addition, the simulation study shows that underestimating demand is marginally more costly than overestimating it, based on the comparative analysis of all instances. Furthermore, the results indicate that the MRP safety stock exploitation heuristic can mitigate the negative effects of biased forecasts.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103115"},"PeriodicalIF":3.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761236","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":"An innovative dual-phased synergistic energy management approach for WSNs using enhanced sleep/awake scheduling and adaptive routing process","authors":"Michaelraj Kingston ROBERTS ,&nbsp;Jeevanandham S ,&nbsp;Jaime Lloret ,&nbsp;Fadl Dahan","doi":"10.1016/j.simpat.2025.103120","DOIUrl":"10.1016/j.simpat.2025.103120","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) have established themselves as one of the essential technologies in various applications, yet they face significant challenges due to their limited energy resources. To overcome this shortcoming, this work introduces an innovative dual-phased synergistic energy management approach that integrates enhanced sleep/awake scheduling based on Multi-Objective Particle Swarm Optimization with Crowding Distance (MOPSO<img>CD) and Reservoir Computing (RC) based adaptive routing for optimizing energy consumption using dynamic real time-based node state adjustment mechanism. Experimental outcomes obtained through comprehensive simulations indicate that our proposed methodology achieves up to 32 % reduction in energy consumption per node, a 50 % improvement in extending network lifetime, and a 11 % enhancement in Packet Delivery Ratio (PDR) compared to state-of-the art algorithms. Additionally, the proposed method minimizes the computational overhead by 40 % which ensures reliability in dynamic environmental conditions. This outstanding performance is attributed to the intelligent integration of RC-driven energy predictions with adaptive routing and optimized clustering, which offers significant advancement in energy management strategies for WSNs, paving the path for sustainable and reliable network deployment.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103120"},"PeriodicalIF":3.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761234","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}