Pub Date : 2026-02-01Epub Date: 2025-12-16DOI: 10.1016/j.simpat.2025.103246
Pasquale Legato, Massimiliano Matteucci, Rina Mary Mazza
The (re)organization of existing and planned warehouse facilities typically seeks to balance system-centric performance indicators (e.g., resource productivity) with customer-focused metrics (e.g., order response time). In pursuit of this objective, there is a growing opportunity to transition from conventional simulation models toward digital twin solutions, which offer enhanced decision-support capabilities. This manuscript focuses on the reorganization of manually executed order picking within a real-world wholesale operation. A simulation-based framework is introduced within a digital shadow environment to optimize the order picking process, following an S-shaped, person-to-goods picking strategy. At the core of the modeling approach is an enriched event graph, which captures the manual picking process at a fine-grained level by representing operational events and their real-time interdependencies. To demonstrate the framework’s effectiveness, numerical results are presented for a typical workday in a major Italian retail cooperative. These results compare alternative control policies, examining their impact on queueing dynamics and picker interference, key contributors to service blocking, resource locking, and starvation within the warehouse’s parallel picking aisles.
{"title":"Towards a digital twin solution for manual order-picking operations in a wholesale distribution center","authors":"Pasquale Legato, Massimiliano Matteucci, Rina Mary Mazza","doi":"10.1016/j.simpat.2025.103246","DOIUrl":"10.1016/j.simpat.2025.103246","url":null,"abstract":"<div><div>The (re)organization of existing and planned warehouse facilities typically seeks to balance system-centric performance indicators (e.g., resource productivity) with customer-focused metrics (e.g., order response time). In pursuit of this objective, there is a growing opportunity to transition from conventional simulation models toward digital twin solutions, which offer enhanced decision-support capabilities. This manuscript focuses on the reorganization of manually executed order picking within a real-world wholesale operation. A simulation-based framework is introduced within a digital shadow environment to optimize the order picking process, following an S-shaped, person-to-goods picking strategy. At the core of the modeling approach is an enriched event graph, which captures the manual picking process at a fine-grained level by representing operational events and their real-time interdependencies. To demonstrate the framework’s effectiveness, numerical results are presented for a typical workday in a major Italian retail cooperative. These results compare alternative control policies, examining their impact on queueing dynamics and picker interference, key contributors to service blocking, resource locking, and starvation within the warehouse’s parallel picking aisles.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"147 ","pages":"Article 103246"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791294","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 : 2026-02-01Epub Date: 2025-12-24DOI: 10.1016/j.simpat.2025.103249
Hui Sun , Honghui Song , Chaoye Fang , Qiaorui Si , Yu Wu , Shouqi Yuan
Pumped storage power stations face significant efficiency and safety challenges due to complex hydraulic and mechanical faults. While accurate fault diagnosis is critical for reliable operation, existing data-driven methods often lack generalizability and explicit integration of domain knowledge in multi-fault scenarios. To address this, we develop a novel simulation-knowledge hybrid framework that integrates physics-based representations from Computational Fluid Dynamics (CFD) / Finite Element Method (FEM) simulations with a data-driven Stacked Denoising Auto-Encoder (SDAE) model enhanced by RIME optimization algorithm. The framework introduces a systematic knowledge-formalization and feature-alignment mechanism that translates implicit physical fields into quantifiable indicators aligned with experimental features. Through detailed hydraulic and mechanical simulations, we characterize cavitation and bearing wear fault signatures, formalizing them into traceable diagnostic datasets. This establishes a transparent evidence chain linking fault diagnoses to underlying physical mechanisms. To improve generalization, the SDAE model undergoes autonomous hyperparameter adaptation via the RIME algorithm, enhancing its capability to interpret hybrid knowledge-data inputs. Experimental validation demonstrates that the knowledge-integrated RIME-SDAE model achieves near-perfect identification accuracy exceeding 99%, outperforming both baseline SDAE (93.3%) and SVM models. Field tests confirm the framework's robustness and accuracy, enabling real-time fault traceability without additional sensors. This research provides a scalable methodology for enhancing operational reliability and supporting design decisions in pumped storage power stations through explicit knowledge utilization and autonomous model adaptation.
{"title":"A simulation-knowledge traceability and data-driven framework with autonomous optimization for multi-fault diagnosis in the pumped storage power stations","authors":"Hui Sun , Honghui Song , Chaoye Fang , Qiaorui Si , Yu Wu , Shouqi Yuan","doi":"10.1016/j.simpat.2025.103249","DOIUrl":"10.1016/j.simpat.2025.103249","url":null,"abstract":"<div><div>Pumped storage power stations face significant efficiency and safety challenges due to complex hydraulic and mechanical faults. While accurate fault diagnosis is critical for reliable operation, existing data-driven methods often lack generalizability and explicit integration of domain knowledge in multi-fault scenarios. To address this, we develop a novel simulation-knowledge hybrid framework that integrates physics-based representations from Computational Fluid Dynamics (CFD) / Finite Element Method (FEM) simulations with a data-driven Stacked Denoising Auto-Encoder (SDAE) model enhanced by RIME optimization algorithm. The framework introduces a systematic knowledge-formalization and feature-alignment mechanism that translates implicit physical fields into quantifiable indicators aligned with experimental features. Through detailed hydraulic and mechanical simulations, we characterize cavitation and bearing wear fault signatures, formalizing them into traceable diagnostic datasets. This establishes a transparent evidence chain linking fault diagnoses to underlying physical mechanisms. To improve generalization, the SDAE model undergoes autonomous hyperparameter adaptation via the RIME algorithm, enhancing its capability to interpret hybrid knowledge-data inputs. Experimental validation demonstrates that the knowledge-integrated RIME-SDAE model achieves near-perfect identification accuracy exceeding 99%, outperforming both baseline SDAE (93.3%) and SVM models. Field tests confirm the framework's robustness and accuracy, enabling real-time fault traceability without additional sensors. This research provides a scalable methodology for enhancing operational reliability and supporting design decisions in pumped storage power stations through explicit knowledge utilization and autonomous model adaptation.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"147 ","pages":"Article 103249"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884625","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 : 2026-01-01Epub Date: 2025-11-14DOI: 10.1016/j.simpat.2025.103225
Buchuan Zhang , Chuan-Zhi Thomas Xie
As a distinct form of pedestrian motion, skiing possesses a long-standing history, yet the recurrent occurrence of ski-related accidents underscores the necessity of deeper inquiry into this dynamic system. In light of such a need, the present study adopts a modelling and simulation perspective to construct a framework for analysing skier trajectories and performance, with explicit consideration of the complex interactions between human behaviour, varying environmental and physical conditions. To this end, in specific, a cellular automaton (CA)-based model was developed, incorporating six critical factors: slope angle, surface friction, boundary constraints, terrain curvature, aerodynamic drag, and directional inertia. Probabilistic decision rules combined with physics-based speed updates enabled realistic skier movement simulations across a discretized slope grid. The simulation shows that slope angle predominantly drives skier speed, while surface friction and aerodynamic drag reduce efficiency by increasing resistance and prolonging descent. Boundary effects, though minor under wide-slope conditions, help confine lateral motion and influence path shaping. Terrain curvature impacts turning dynamics, especially on rough or irregular surfaces, while inertia enhances straight-line speed but reduces adaptability. The study underscores the importance of capturing both environmental and behavioural interactions to accurately model downhill skiing dynamics and provides detailed insights into the mechanisms shaping skiing efficiency, offering a powerful tool for advanced skier simulation and slope performance analysis. This study presents a cellular automaton (CA)-based modelling framework for simulating skier dynamics. Model integrates six environmental factors – slope, friction, boundary, curvature, aerodynamic drag, and inertia – to reproduce realistic motion patterns on alpine slopes. This study primarily focuses on the dynamics of a single skier, while multi-agent interactions will be explored in future work.
{"title":"Exploring the complexity of pedestrian dynamics in skiing: A modelling and simulation framework","authors":"Buchuan Zhang , Chuan-Zhi Thomas Xie","doi":"10.1016/j.simpat.2025.103225","DOIUrl":"10.1016/j.simpat.2025.103225","url":null,"abstract":"<div><div>As a distinct form of pedestrian motion, skiing possesses a long-standing history, yet the recurrent occurrence of ski-related accidents underscores the necessity of deeper inquiry into this dynamic system. In light of such a need, the present study adopts a modelling and simulation perspective to construct a framework for analysing skier trajectories and performance, with explicit consideration of the complex interactions between human behaviour, varying environmental and physical conditions. To this end, in specific, a cellular automaton (CA)-based model was developed, incorporating six critical factors: slope angle, surface friction, boundary constraints, terrain curvature, aerodynamic drag, and directional inertia. Probabilistic decision rules combined with physics-based speed updates enabled realistic skier movement simulations across a discretized slope grid. The simulation shows that slope angle predominantly drives skier speed, while surface friction and aerodynamic drag reduce efficiency by increasing resistance and prolonging descent. Boundary effects, though minor under wide-slope conditions, help confine lateral motion and influence path shaping. Terrain curvature impacts turning dynamics, especially on rough or irregular surfaces, while inertia enhances straight-line speed but reduces adaptability. The study underscores the importance of capturing both environmental and behavioural interactions to accurately model downhill skiing dynamics and provides detailed insights into the mechanisms shaping skiing efficiency, offering a powerful tool for advanced skier simulation and slope performance analysis. This study presents a cellular automaton (CA)-based modelling framework for simulating skier dynamics. Model integrates six environmental factors – slope, friction, boundary, curvature, aerodynamic drag, and inertia – to reproduce realistic motion patterns on alpine slopes. This study primarily focuses on the dynamics of a single skier, while multi-agent interactions will be explored in future work.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103225"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579565","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}
Cold spray additive manufacturing (CSAM) is an emerging solid-state deposition technique that utilizes high-velocity gas to propel powdered materials onto a substrate. Analysis of objective functions for process parameter optimization in CSAM requires data that is usually obtained from costly experiments or numerical simulations. Integrating simulations or experiments directly into conventional optimization algorithms can lead to significantly high computational costs. Additionally, these optimization problems typically involve multiple conflicting objectives that should be taken into account simultaneously. In this work, we develop a data-driven, simulation-based multi-objective optimization framework (SMOF) to optimize CSAM process parameters online. The smoothed particle hydrodynamics (SPH) method is used to perform CSAM simulations. A new optimal grid mutation-based infill criterion (OIC) is proposed to enhance the surrogate-assisted search in SMOF. Subsequently, numerical simulations are replaced by an ensemble of surrogates with high prediction robustness. We assess the effectiveness of the proposed OIC on two benchmark test problems and further optimize multiple powder impact problems. The optimization results demonstrate that the present SMOF can identify desired process parameter combinations for the CSAM process. Based on the proposed SMOF, refined multi-objective process parameter windows are established for the first time to analyze the evolution of CSAM process parameters.
{"title":"Optimizing process parameters in cold spray additive manufacturing: A data-driven, simulation-based multi-objective approach","authors":"Hao Chen , Zhilang Zhang , Markus Bambach , Mohamadreza Afrasiabi","doi":"10.1016/j.simpat.2025.103235","DOIUrl":"10.1016/j.simpat.2025.103235","url":null,"abstract":"<div><div>Cold spray additive manufacturing (CSAM) is an emerging solid-state deposition technique that utilizes high-velocity gas to propel powdered materials onto a substrate. Analysis of objective functions for process parameter optimization in CSAM requires data that is usually obtained from costly experiments or numerical simulations. Integrating simulations or experiments directly into conventional optimization algorithms can lead to significantly high computational costs. Additionally, these optimization problems typically involve multiple conflicting objectives that should be taken into account simultaneously. In this work, we develop a data-driven, simulation-based multi-objective optimization framework (SMOF) to optimize CSAM process parameters online. The smoothed particle hydrodynamics (SPH) method is used to perform CSAM simulations. A new optimal grid mutation-based infill criterion (OIC) is proposed to enhance the surrogate-assisted search in SMOF. Subsequently, numerical simulations are replaced by an ensemble of surrogates with high prediction robustness. We assess the effectiveness of the proposed OIC on two benchmark test problems and further optimize multiple powder impact problems. The optimization results demonstrate that the present SMOF can identify desired process parameter combinations for the CSAM process. Based on the proposed SMOF, refined multi-objective process parameter windows are established for the first time to analyze the evolution of CSAM process parameters.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103235"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623774","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 : 2026-01-01Epub Date: 2025-11-12DOI: 10.1016/j.simpat.2025.103228
Agostino Bruzzone , Alessia Giulianetti , Marco Gotelli , Anna Sciomachen
In this paper, we analyze different scenarios for container flows arriving at marine terminals to different destinations in the hinterland. The aim of the study is to verify how the type of import containers — standard, hazardous, and refrigerated — and their size affect the operational efficiency of the terminal. Relevant performance indicators, such as container dwell time, average and maximum number of waiting containers, and equipment utilization rate, are evaluated. To this end, we present a discrete-event simulation study that, although generalizable to any port, refers to a terminal in the port network of Genoa (Italy). The number of considered scenarios, illustrated in this paper, are taken from a synthetic data generator for logistics flows and used in Witness Horizon v.24 simulation software environment to execute independent runs at a steady state condition. To the authors’ knowledge, this is the first time that a sensitivity analysis based on the variation in the types of containers is presented. The performed simulation experiments can be of great interest to various port stakeholders. Indeed, the results show that the percentage composition of the type of import container over the annual time horizon considered has an impact on the indicators under analysis, favoring a more balanced distribution. However, again in relation to the same indicators, the variation in container size appears to be negligible. The study highlights how advance knowledge of the type of import containers can support port terminal management in terms of efficient management and optimization of resources, providing specific advice on the operational decisions concerning equipment and block yard allocation.
{"title":"The impact of import container flow characteristics on port operational efficiency","authors":"Agostino Bruzzone , Alessia Giulianetti , Marco Gotelli , Anna Sciomachen","doi":"10.1016/j.simpat.2025.103228","DOIUrl":"10.1016/j.simpat.2025.103228","url":null,"abstract":"<div><div>In this paper, we analyze different scenarios for container flows arriving at marine terminals to different destinations in the hinterland. The aim of the study is to verify how the type of import containers — standard, hazardous, and refrigerated — and their size affect the operational efficiency of the terminal. Relevant performance indicators, such as container dwell time, average and maximum number of waiting containers, and equipment utilization rate, are evaluated. To this end, we present a discrete-event simulation study that, although generalizable to any port, refers to a terminal in the port network of Genoa (Italy). The number of considered scenarios, illustrated in this paper, are taken from a synthetic data generator for logistics flows and used in Witness Horizon v.24 simulation software environment to execute independent runs at a steady state condition. To the authors’ knowledge, this is the first time that a sensitivity analysis based on the variation in the types of containers is presented. The performed simulation experiments can be of great interest to various port stakeholders. Indeed, the results show that the percentage composition of the type of import container over the annual time horizon considered has an impact on the indicators under analysis, favoring a more balanced distribution. However, again in relation to the same indicators, the variation in container size appears to be negligible. The study highlights how advance knowledge of the type of import containers can support port terminal management in terms of efficient management and optimization of resources, providing specific advice on the operational decisions concerning equipment and block yard allocation.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103228"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529386","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 : 2026-01-01Epub Date: 2025-11-22DOI: 10.1016/j.simpat.2025.103231
Helen D. Karatza
Cooperating cloud-fog-mist computing frameworks have been methodically designed to balance computational efficiency and data privacy during the execution of complex applications with diverse security demands. To guarantee the proper execution of these applications, the implementation of security-aware scheduling strategies is crucial. This paper explores security-aware scheduling policies, with a focus on developing algorithms tailored for heterogeneous workloads, including both simple single-task jobs and Bags of Linear Workflows (BoLWs) with varying priority levels. Multi-criteria scheduling algorithms are utilized to handle tasks by priority in the three layers. These algorithms are evaluated under different conditions, including varying system utilization, security requirements, and task service demands. Building on the epoch policy discussed in prior research, which considers job security levels, we propose an enhanced epoch-based approach that also accounts for the number of virtual machines allocated to each BoLW job alongside its security requirements. Simulation results demonstrate the superior performance of this novel epoch strategy compared to the previously established approach.
{"title":"Scheduling mixed workloads with security requirements in a cloud-fog-mist computing environment","authors":"Helen D. Karatza","doi":"10.1016/j.simpat.2025.103231","DOIUrl":"10.1016/j.simpat.2025.103231","url":null,"abstract":"<div><div>Cooperating cloud-fog-mist computing frameworks have been methodically designed to balance computational efficiency and data privacy during the execution of complex applications with diverse security demands. To guarantee the proper execution of these applications, the implementation of security-aware scheduling strategies is crucial. This paper explores security-aware scheduling policies, with a focus on developing algorithms tailored for heterogeneous workloads, including both simple single-task jobs and Bags of Linear Workflows (BoLWs) with varying priority levels. Multi-criteria scheduling algorithms are utilized to handle tasks by priority in the three layers. These algorithms are evaluated under different conditions, including varying system utilization, security requirements, and task service demands. Building on the epoch policy discussed in prior research, which considers job security levels, we propose an enhanced epoch-based approach that also accounts for the number of virtual machines allocated to each BoLW job alongside its security requirements. Simulation results demonstrate the superior performance of this novel epoch strategy compared to the previously established approach.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103231"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623771","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 : 2026-01-01Epub Date: 2025-11-01DOI: 10.1016/j.simpat.2025.103221
Hang Thi Pham , Tien-Thinh Le , Panagiotis G. Asteris
The aim of this study is to investigate the influence of main machining parameters during the turning process of AISI304 stainless steel through experimental work and finite element simulation employing a realistic 3D cylindrical workpiece model. The application of a realistic 3D model facilitates a strong correlation between simulation and experimental findings concerning chip morphology and temperature distribution. Both experimental and simulation results reveal the formation of elongated helical-shaped chips. An increase in cutting depth induces higher stress and equivalent plastic deformation. Meanwhile, a higher cutting speed leads to lower stress distributed on the chip and the machined workpiece. The temperature trends near the cutting tool nose and along the main cutting edge differ considerably. The highest temperature is concentrated on the main cutting edge of the cutting tool during the machining process, reaching up to 1000 °K in the case of high cutting speed and large cutting depth. In contrast, the temperature on the chip and machined surface are about 330 °K and 300 °K, respectively.
{"title":"Experimental investigation and 3D finite element simulation of the turning process for AISI304 stainless steel","authors":"Hang Thi Pham , Tien-Thinh Le , Panagiotis G. Asteris","doi":"10.1016/j.simpat.2025.103221","DOIUrl":"10.1016/j.simpat.2025.103221","url":null,"abstract":"<div><div>The aim of this study is to investigate the influence of main machining parameters during the turning process of AISI304 stainless steel through experimental work and finite element simulation employing a realistic 3D cylindrical workpiece model. The application of a realistic 3D model facilitates a strong correlation between simulation and experimental findings concerning chip morphology and temperature distribution. Both experimental and simulation results reveal the formation of elongated helical-shaped chips. An increase in cutting depth induces higher stress and equivalent plastic deformation. Meanwhile, a higher cutting speed leads to lower stress distributed on the chip and the machined workpiece. The temperature trends near the cutting tool nose and along the main cutting edge differ considerably. The highest temperature is concentrated on the main cutting edge of the cutting tool during the machining process, reaching up to 1000 °K in the case of high cutting speed and large cutting depth. In contrast, the temperature on the chip and machined surface are about 330 °K and 300 °K, respectively.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103221"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145475187","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 : 2026-01-01Epub Date: 2025-10-30DOI: 10.1016/j.simpat.2025.103217
Martina Umlauft, Wilfried Elmenreich, Udo Schilcher
The topology model is an important aspect of wireless mesh network simulation. To test a new protocol typically several different topologies are necessary to perform a statistically significant number of simulation runs. Simply using topologies of real-world networks directly is not sufficient because the number of topology data available is less than the number of topologies required for simulation. Therefore, artificially generated topologies are used. Unfortunately, many simulators use either a uniform node distribution or even just a simple grid topology which both differ significantly from real-world topologies.
We first revisit the differences between uniform and grid topologies vs. real-world topologies using four different real-world networks to motivate the need for such a tool and then present EvoTopo, a new approach to generate more realistic topologies. EvoTopo uses a genetic algorithm to create the desired number of simulation topologies from the node positions of one real-world network. The generated topologies are evolved to be “similar” w.r.t. homogeneity of the node distribution, nearest neighbor distance, and node density. We evaluate our algorithm analyzing average overall node distance, the degree distribution of nodes, and the performance of a simple flooding algorithm and compare our algorithm to other approaches. The EvoTopo tool and the four sample topologies can be downloaded from our homepage; generated topologies are written to a simple text file which can be imported into a simulator of choice.
{"title":"Evolving realistic topologies for wireless mesh network simulation with EvoTopo","authors":"Martina Umlauft, Wilfried Elmenreich, Udo Schilcher","doi":"10.1016/j.simpat.2025.103217","DOIUrl":"10.1016/j.simpat.2025.103217","url":null,"abstract":"<div><div>The topology model is an important aspect of wireless mesh network simulation. To test a new protocol typically several different topologies are necessary to perform a statistically significant number of simulation runs. Simply using topologies of real-world networks directly is not sufficient because the number of topology data available is less than the number of topologies required for simulation. Therefore, artificially generated topologies are used. Unfortunately, many simulators use either a uniform node distribution or even just a simple grid topology which both differ significantly from real-world topologies.</div><div>We first revisit the differences between uniform and grid topologies vs. real-world topologies using four different real-world networks to motivate the need for such a tool and then present <span>EvoTopo</span>, a new approach to generate more realistic topologies. <span>EvoTopo</span> uses a genetic algorithm to create the desired number of simulation topologies from the node positions of one real-world network. The generated topologies are evolved to be “similar” w.r.t. homogeneity of the node distribution, nearest neighbor distance, and node density. We evaluate our algorithm analyzing average overall node distance, the degree distribution of nodes, and the performance of a simple flooding algorithm and compare our algorithm to other approaches. The <span>EvoTopo</span> tool and the four sample topologies can be downloaded from our homepage; generated topologies are written to a simple text file which can be imported into a simulator of choice.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103217"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145475186","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 : 2026-01-01Epub Date: 2025-10-29DOI: 10.1016/j.simpat.2025.103219
Laura De Natale, Georgia Fargetta, Laura R.M. Scrimali, Sebastiano Battiato
Global disasters increasingly disrupt agricultural commodity flows, with food insecurity as a major consequence. Quantitative tools to assess such impacts are essential for resilience. We propose a hybrid Multi-Agent Reinforcement Learning (MARL) architecture to solve Variational Inequality (VI) problems in multi-commodity trade equilibria. While variational inequalities offer a rigorous method, their resolution via MARL faces stability and convergence challenges. Our actor–critic approach integrates a Gradient-based Learning Rate (GLR) scheduler, adaptive epsilon decay, prioritized replay, and a dual reward combining individual and centralized feedback. Agents representing supply and demand learn optimal strategies to reach equilibrium in simulated markets. Experiments, spanning stable conditions, dynamic price shifts, and route blockages, show faster convergence and stronger robustness than Multi-Agent Proximal Policy Optimization (MAPPO) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Results highlight the promise of MARL for simulating economic behavior and optimizing decentralized decision-making in complex systems.
{"title":"Multi-agent reinforcement learning and variational inequality models for international trade networks under crisis","authors":"Laura De Natale, Georgia Fargetta, Laura R.M. Scrimali, Sebastiano Battiato","doi":"10.1016/j.simpat.2025.103219","DOIUrl":"10.1016/j.simpat.2025.103219","url":null,"abstract":"<div><div>Global disasters increasingly disrupt agricultural commodity flows, with food insecurity as a major consequence. Quantitative tools to assess such impacts are essential for resilience. We propose a hybrid Multi-Agent Reinforcement Learning (MARL) architecture to solve Variational Inequality (VI) problems in multi-commodity trade equilibria. While variational inequalities offer a rigorous method, their resolution via MARL faces stability and convergence challenges. Our actor–critic approach integrates a Gradient-based Learning Rate (GLR) scheduler, adaptive epsilon decay, prioritized replay, and a dual reward combining individual and centralized feedback. Agents representing supply and demand learn optimal strategies to reach equilibrium in simulated markets. Experiments, spanning stable conditions, dynamic price shifts, and route blockages, show faster convergence and stronger robustness than Multi-Agent Proximal Policy Optimization (MAPPO) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Results highlight the promise of MARL for simulating economic behavior and optimizing decentralized decision-making in complex systems.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"146 ","pages":"Article 103219"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145398229","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 : 2026-01-01Epub Date: 2025-10-28DOI: 10.1016/j.simpat.2025.103220
Doudou Si, Zuanfeng Pan, Wendi Li
The quantification of blast load on a building constitutes a pivotal determinant in blast-resistant structural engineering. When the explosion occurs in a complex urban environment, the blast load on the building surface is conventionally obtained through scaled explosion tests or computational fluid dynamics simulations. Given the significant expenses of explosion tests and numerical simulations, this study innovatively applies the long short-term memory (LSTM) network to predict the blast load time history in urban explosion scenarios. The main idea is to establish an LSTM network to learn the temporal and spatial variations of blast loads from limited training data and predict the time history of blast loads at unmonitored locations or unknown explosion scenarios. In particular, a unidirectional multi-layer stacked LSTM architecture was used, and recursive computation was performed using a sliding window. The optimal hyperparameters for the model were determined through Bayesian optimization. The performance of the LSTM network was validated through numerical simulations of an explosion in a straight urban street. The results demonstrate that the LSTM network can accurately predict the multi-peak characteristics of blast loads in the street and the arrival times of each peak, demonstrating significant potential for blast load time histories prediction in complex environments.
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