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.
{"title":"Insights into the metal cutting contact zone through automation and multivariate regression modelling under the framework of gear skiving","authors":"Florian Sauer , Amartya Mukherjee , 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}
Pub Date : 2025-04-15DOI: 10.1016/j.simpat.2025.103122
Xu Sun , Ming Yue , Heyang Wang , Yang Liu , Xudong Zhao
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.
{"title":"An integrated framework for Multi-AMR based CL-CBS and MPC-APF in warehousing scenario","authors":"Xu Sun , Ming Yue , Heyang Wang , Yang Liu , 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}
Pub Date : 2025-04-10DOI: 10.1016/j.simpat.2025.103121
Subhra Priyadarshini Biswal, Sanjeev Patel
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.
{"title":"Optimization of Veno parameter based on stochastic approximation: OVeno","authors":"Subhra Priyadarshini Biswal, 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}
Pub Date : 2025-04-08DOI: 10.1016/j.simpat.2025.103118
Xiaoran Kong , Jianyong Yang , Xinghua Chai , Yatong Zhou
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.
{"title":"An advantage duPLEX dueling multi-agent Q-learning algorithm for multi-UAV cooperative target search in unknown environments","authors":"Xiaoran Kong , Jianyong Yang , Xinghua Chai , 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}
Pub Date : 2025-04-07DOI: 10.1016/j.simpat.2025.103119
Carla Pineda, Alfredo Santana, Rafael Batres
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.
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.
{"title":"An iterative surrogate-based optimization approach for multi-server queuing system design","authors":"Carla Pineda, Alfredo Santana, 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}
Pub Date : 2025-04-04DOI: 10.1016/j.simpat.2025.103124
Jing Guo, Jin Qi, Jie Hu, Chengan Hong, Yuliang Shen, Haiqing Huang, Weijie Liu
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.
{"title":"Robust optimization method for co-simulation of equipment based on EDEM-ADAMS","authors":"Jing Guo, Jin Qi, Jie Hu, Chengan Hong, Yuliang Shen, Haiqing Huang, 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}
Pub Date : 2025-03-28DOI: 10.1016/j.simpat.2025.103115
Wolfgang Seiringer , Klaus Altendorfer , Thomas Felberbauer , Balwin Bokor , Fabian Brockmann
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.
{"title":"How periodic forecast updates influence MRP planning parameters: A simulation study","authors":"Wolfgang Seiringer , Klaus Altendorfer , Thomas Felberbauer , Balwin Bokor , 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}
Pub Date : 2025-03-27DOI: 10.1016/j.simpat.2025.103120
Michaelraj Kingston ROBERTS , Jeevanandham S , Jaime Lloret , Fadl Dahan
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 (MOPSOCD) 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.
{"title":"An innovative dual-phased synergistic energy management approach for WSNs using enhanced sleep/awake scheduling and adaptive routing process","authors":"Michaelraj Kingston ROBERTS , Jeevanandham S , Jaime Lloret , 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}
Pub Date : 2025-03-25DOI: 10.1016/j.simpat.2025.103117
Jingyu Tan , Wenke Zhang , Tingting Nong , Zhichao Zhang , Tao Wang , Yi Ma , Eric Wai Ming Lee , Meng Shi
Accurately simulating pedestrian behaviour under different visibility conditions is crucial for reducing casualties during emergency fire evacuations. Current research employing social force models typically simulates pedestrian behaviour based on interaction forces under specific visibility conditions. However, these studies often inadequately capture the dynamic effects of visibility changes on pedestrian interaction forces. Based on the nucleus force theory, this study developed an improved social force model (NSFM), incorporating environmental visibility parameters, establishing corresponding pedestrian movement rules. Additionally, we investigated the interaction between pedestrians and walls to determine the optimal parameters. The model’s accuracy was then validated by comparing its simulations under specific visibility conditions from previous visibility-based evacuation experiments and results from other models. Furthermore, we conducted simulations under different visibility conditions, the results show that reduced visibility intensifies wall-following behaviour and herd effects, leading to more detour behaviour, slower movement velocity, and longer evacuation times. As visibility increases, the impact on evacuation gradually diminishes. Finally, we investigated the impact of the number and location of exits and discovered that increasing the number substantially reduces evacuation time, while changes in exit locations can notably affect evacuation efficiency. The numerical simulation results demonstrate that the NSFM has significant potential for simulating pedestrian evacuation behaviour and processes under different visibility conditions, providing a scientific basis for designing more effective evacuation strategies in the future.
{"title":"An improved social force model based on nucleus force theory to simulate building evacuation in different visibility conditions","authors":"Jingyu Tan , Wenke Zhang , Tingting Nong , Zhichao Zhang , Tao Wang , Yi Ma , Eric Wai Ming Lee , Meng Shi","doi":"10.1016/j.simpat.2025.103117","DOIUrl":"10.1016/j.simpat.2025.103117","url":null,"abstract":"<div><div>Accurately simulating pedestrian behaviour under different visibility conditions is crucial for reducing casualties during emergency fire evacuations. Current research employing social force models typically simulates pedestrian behaviour based on interaction forces under specific visibility conditions. However, these studies often inadequately capture the dynamic effects of visibility changes on pedestrian interaction forces. Based on the nucleus force theory, this study developed an improved social force model (NSFM), incorporating environmental visibility parameters, establishing corresponding pedestrian movement rules. Additionally, we investigated the interaction between pedestrians and walls to determine the optimal parameters. The model’s accuracy was then validated by comparing its simulations under specific visibility conditions from previous visibility-based evacuation experiments and results from other models. Furthermore, we conducted simulations under different visibility conditions, the results show that reduced visibility intensifies wall-following behaviour and herd effects, leading to more detour behaviour, slower movement velocity, and longer evacuation times. As visibility increases, the impact on evacuation gradually diminishes. Finally, we investigated the impact of the number and location of exits and discovered that increasing the number substantially reduces evacuation time, while changes in exit locations can notably affect evacuation efficiency. The numerical simulation results demonstrate that the NSFM has significant potential for simulating pedestrian evacuation behaviour and processes under different visibility conditions, providing a scientific basis for designing more effective evacuation strategies in the future.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"142 ","pages":"Article 103117"},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746989","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}
Pub Date : 2025-03-25DOI: 10.1016/j.simpat.2025.103116
Punit Bhanwar, Trudeep N. Dave
Analyzing various geotechnical problems associated with cohesionless soils necessitates the creation of a physical model exhibiting a uniformly reconstituted soil structure. However, reconstituting such a well-conditioned model with complex reconstitution criteria or goals using the conventional air pluviation technique is an intricate task. To address this challenge, the present study proposes a novel Spreadsheet-based Pluviation Parametric Optimizer (SPPO), which is purposely developed for the systematized, controlled, and optimal reconstitution of cohesionless soils. A cohesive integration of a Mechatronic-Assisted Air Pluviation System (MAPS) and the Taguchi-VIKOR-based Multi-response Optimization (TV-MRO) algorithm, devised through SPPO, is utilized to achieve the aforementioned optimal reconstitution. To verify the competence of the decision support framework offered by SPPO, the reconstitution of a poorly graded quartz sand (D50=0.22 mm) considering five modelled reconstitution scenarios was conducted. For each modelled reconstitution scenario, SPPO identified an optimal pluviation setting, which was further validated by a confirmatory experiment and a devised optimality criterion. Additionally, it was revealed that the height of fall is the most influential parameter in optimal reconstitution, followed by diffuser ratio and sieve porosity. Overall, it is anticipated that the adoption of SPPO can be effective in reconstituting cohesionless soils with much less reconstitution effort, time, and resource deployment compared to conventional reconstitution methodologies.
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