SIMULATION

Computer simulation has become increasingly complex and widely applied across different domains. However, the reproducibility and replicability (R&R) of simulation models remain limited. Despite recent improvements, independent reproduction or replication of simulation experiments is rare in the literature. This paper provides an overview of the state of research on R&R in simulation, highlights recent developments, and discusses key concepts and future perspectives. It first examines how R&R has been viewed, approached, and evaluated and then outlines typical challenges and defining characteristics of R&R. Emerging opportunities are also discussed in light of community-driven practices, artificial intelligence, and quantum computing. Given the significant role of simulation in modern science, this paper argues that R&R studies of simulation are valuable research outputs and should be regarded as an integral and equally important part of scientific progress. R&R should be explicitly addressed and embedded into modelling and simulation practices, and supported by stronger community efforts. Researchers engaging in these efforts face substantial challenges, including those related to recognition and rewards, methodology, and scalability, many of which are under-researched.

The scheduling of offshore wind farm (OWF) installations is a complex, weather-driven, and resource-constrained problem. Mixed-integer linear programming (MILP) models excel at cost and makespan optimization, but remain difficult to verify and validate beyond feasibility checks. Similarly, Petri nets (PNs) provide behavioral transparency and simulation fidelity, yet lack prescriptive optimality. This article presents frameworks that utilize complementary application strategies to address two key needs, thereby combining the strengths of both approaches. First, an iterative verification–validation framework is employed that derives local PN models from MILP solutions and compares them with knowledge-based PN representations using conformance and reachability checks. Implemented in a simulation environment for the installation of OWF, the framework enables consistent model verification, cross-model validation, and context-aware scheduling. Second, a cascading decision-support framework that selects or blends MILP, heuristic, and PN-based scheduling methods through standardized descriptors and context signals. Numerical experiments demonstrate that optimization minimizes offshore time and cost, but requires high computational efforts. In contrast, Heuristics deliver short-term plans rapidly, while PN-based schedules offer intermediate costs and longer planning horizons, demonstrating that hybrid, context-sensitive orchestration outperforms any single method.

Modern network infrastructures are becoming more concerned about energy consumption, especially with software-defined networks (SDNs). Because SDN separates the control plane from the data plane, it allows for programmability in networks, which is not possible with conventional networks. Conventional routing techniques usually lead to excessive power consumption because they handle traffic. However, numerous frameworks have been presented to address this, with a focus on utility-based and machine learning techniques for traffic-aware energy optimization in SDN. These methods, however, often overlook the combined effect of dynamic routing and intelligent traffic classification for energy savings. The research paper proposes a new framework called EESDN that presents an energy-efficient routing (EER) algorithm that uses intelligent traffic classification to allocate paths in an energy-efficient manner. The objective is to maintain network performance while reducing energy consumption. For the SDN energy-saving opportunity identification issue, we propose an integer linear programming (ILP) approach. Using real-world traffic traces with the Mininet emulator and POX controller, the Abilene network architecture is examined. Our model shows a potential reduction in active network components, which, under idealized conditions, could translate to power savings of 8 to 25 W in our emulation and decreased average path length under high-traffic conditions. The emulation results demonstrate that the framework reduces energy consumption per bit by approximately 10% compared with existing methods.

Primary healthcare (PHC) systems face unsustainable pressure from population aging and the burden of chronic diseases, creating critical workflow inefficiencies and affecting patient care. This challenge particularly affects chronic patient follow-up, where case managers are often overwhelmed by excessive workloads and the complexities of care coordination. In this context, discrete-event simulation (DES) is essential for evaluating the impact of new health policies before implementation. However, simulating these processes at scale entails high computational costs, limiting the feasibility of standard DES approaches for exploring large-scale PHC system scenarios. To address this challenge, we propose a parallel discrete event simulation (PDES) framework designed explicitly for PHC systems. Our proposal implements an approximate parallel simulation protocol based on the bulk synchronous parallel (BSP) model, enhancing efficiency by simplifying event ordering and eliminating rollback. Validation was conducted by the assessment of healthcare experts. We show that this approach achieves significant scalability (up to a 6.95-fold speedup) while maintaining high fidelity. More than 90% of the tested parallel configurations produced results identical to sequential models, with all outcomes remaining clinically accurate. This validates the protocol’s use for strategic public health decision-making.

Simulation modeling is widely used to investigate the impact of interventions on infectious disease transmission. However, individual-based models often involve numerous parameters, making exhaustive exploration computationally prohibitive. Metamodels can address this limitation, and when coupled with interpretable machine learning, they can provide insights into intervention impacts. Moreover, multiple features of epidemic curves should be considered when evaluating interventions. Using the open-source agent-based model Covasim, we examined non-pharmaceutical interventions affecting key epidemic curve features, such as peak day, peak value, and attack rate. We employed random forests as a metamodeling technique to capture nonlinear input–output relationships. Then, we used the Boruta algorithm to identify impactful interventions. Results showed that random forest metamodels explained moderate to high variance across outputs. The Boruta method indicated that influential interventions differed by outcome. Social distancing measures, such as school closures and workplace density reductions, were mostly unimportant in the current Covasim setting, whereas intervention timing, testing, contact tracing, and mask use consistently influenced all outputs to varying degrees. Our metamodel- and variable selection-based framework can provide insights into the effectiveness of multiple interventions across various characteristics of the epidemic curve, thereby supporting public health decision-making at the onset of an epidemic.

An elliptical hole can cover several different defect shapes within a material, ranging from thin cracks to round holes. In this study, an elliptical-hyperbolic coordinate system was used to mesh a geometry with traction-free elliptical holes. A new transformation was applied to map the physical domain to a computational Cartesian domain, in which the finite difference technique was applied. A few test cases involving ellipses of various shapes, from an almost round hole to a crack, were analyzed numerically. They were subjected to unidirectional and bidirectional loads far away from the hole location. The numerical and exact analytical results showed a good agreement. The stress intensity factors of a central crack and an edge crack subjected to unidirectional loading were extracted by correlating the normal stress ahead of the crack tip and the vertical crack face displacement behind the crack tip using three terms of the asymptotic expansion. The accuracy of both methods was excellent, with an error <1%. The good agreement between the numerical and analytical results confirms the reliability of the proposed technique and paves the way for addressing more complex geometries and material models.

This paper proposes a traffic modeling and a simulation methodology to assist the dimensioning of the capacity of advanced wireless systems. Knowingly, such systems accommodate a diversity of services with corresponding different bandwidth and traffic characteristics, which leads to intricate dimensioning solutions. In our methodology, we treat each type of traffic using its proper model and bundle them in a simulation tool. A fundamental contribution of this work is the proposal of a simulation tool that incorporates general traffic models to yield the performance of the various traffic flows requiring different bandwidths when sharing the same pool of resources in a loss system consisting of a link with a limited capacity. Given the generality and complexity of the model, analytical solutions are not available, and simulation has proven to be a beneficial tool for analyzing and dimensioning the system, with surprising results.

Drilling is an expensive but necessary part of mining, geothermal and oil operations. The cost of drilling can be reduced using simulation models to optimize parameters and perform fatigue prognoses, expanding the useful life of downhole equipment. However, current state-of-the-art simulation models have limitations in modeling drill string dynamics in the build section and generating stress history for drilling and tripping operations. This paper presents a model with an efficient contact algorithm, reconfigurable stiction/friction submodels, accurate boundary conditions, expanding drill string length as the bit advances and readily available stress history for any component. The model predicts the onset and location of wellbore contact, as well as axial, lateral and torsional vibrations in any well section. Multiple well trajectory simulations are presented, showing responses such as whirling and snaking. Stress histories and predicted fatigue life of components are compared, showing how the model can be used for selecting the best well trajectory and drilling parameters during well planning. These case studies also show that dogleg severity and kick-off point of the deviated section influence the fatigue life of the downhole components.

This research uses virtual reality (VR) to immerse human subjects in an active school shooting scenario in order to generate ecologically valid models of school shooter movement and behavior. Historically, data recovered from US school shootings has lacked the fidelity needed to model shooter movement; consequently, simulations of school shootings have had to rely on significant and unsupported assumptions about the movements of the shooter and/or victims. We asked human subjects to act as school shooters in a VR simulation. We then recorded their movements, observations, and actions. Our results show that participant shooters are statistically equivalent to historical incidents with respect to aggregate engagement metrics (shot rate, victim rate, and accuracy) across most scenarios. Moreover, empirical models trained on participant data reduced prediction error by at least 15.5% compared to heuristic baselines and 16.7% compared to models trained on pedestrian data. Overall, this work provides a reproducible framework for data-driven modeling of shooter movement, supporting controlled simulation-based evaluation of response strategies.

As discrete-event systems grow in complexity across domains such as manufacturing, communication, and cyber-physical systems, their modeling and simulation face increasing challenges. Key issues include model reusability, interoperability across heterogeneous simulation platforms, and maintaining consistency of execution semantics. Traditional approaches often rely on simulators bound to specific programming languages, which restricts portability and hinders collaboration across different tools. To overcome these limitations, this paper introduces FPDEVSML, a formal specification language for parallel-DEVS (PDEVS) models. FPDEVSML provides a structured and unambiguous framework for defining system behavior independently of any particular simulator. This language is based on formal foundations in mathematical logic, algebra, and set theory that ensure only well-formed models can be expressed and validated. From a given specification, models are automatically parsed, semantically analyzed, and transformed into a platform-independent representation, which is then used to generate executable code for multiple simulation environments following model-driven engineering (MDE) approach. We demonstrate this approach through the ft4devs (Formal Tool for DEVS) framework, which we developed, providing code generation for both Artis*/C++ and PyPDEVS/Python. Case studies highlight the FPDEVSML language’s expressiveness in modeling parallel and modular systems. The results confirm the feasibility of the approach, bridging the gap between formal modeling and practical simulation for PDEVS modeling formalism, and paving the way for future automatic integration with formal verification.

This paper explores an initial attempt to use the Unity ML-Agents toolkit to model the behavior of people evacuating from indoor fires. The virtual environment was created in the Unity game engine and populated with humanoid agents capable of moving autonomously within the scene. Each agent perceives information from the rendered environment, such as surfaces, directions, and line-of-sight depth and uses it to navigate toward the nearest exit. Agents were trained through reinforcement learning, using the Proximal Policy Optimization (PPO) algorithm to balance rewards and penalties for their actions. We tested five different reward schemes in single-agent simulations to observe how these affect navigation behavior. Among them, the version referred to as mark5 showed the most plausible and efficient evacuation strategy, reaching the exit quickly while avoiding collisions. The same trained agent was then used in multi-agent settings, where its performance remained stable even with groups of up to 20 individuals. These first results suggest that Unity ML-Agents can offer a practical foundation for building more realistic and adaptive evacuation models.

This paper explores the combination of agent-based social simulation (ABSS) models. Model combination facilitates the efficient development of more complex models through reuse, enabling a more comprehensive understanding of phenomena and outcomes that individual models cannot provide on their own. Through a narrative literature review of model combination in other simulation paradigms, six different approaches were identified: ensemble techniques, meta-analysis, model merging, models as modules, model integration and model chains. For each approach, examples and relevant literature are presented, and current challenges are identified. To illustrate the different approaches, a number of models of disease spread are then implemented and combined according to each approach. Through this, the paper aims to both provide inspiration to modelers and to identify paths for future research for the combination of ABSS models and model results.

Reliable, performant code is essential in modeling and simulation, and integration of AI-generated code into simulation software development must not undermine the stability or integrity of models. Generative AI produces variable outputs, resulting in LLM-generated code that does not reliably satisfy standards of correctness or performance. This paper presents an LLM prompt engineering framework, Goal, Performance, Exclusion Architecture (GPE-A), that structures intent, constraints and performance criteria to guide the generation of reliable, performant simulation code. Rather than attempting reproducibility of an implementation, the framework steers generative outputs toward convergence on required behavioral metrics, consistent with requirements-driven development. The framework is evaluated using LLM-generated random number generation code as a representative simulation component across classical and quantum computing. Random number generation is a foundational simulation primitive and domain where quantum methods allow a comparable proof-of-concept implementation against classical implementations. Metric-based comparisons are made to production baselines, assessing correctness, statistical stability, and computational performance. The GPE-A is evaluated across multiple LLMs, through prompt ablation, and against modern prompting methods. Results indicate that structured LLM prompt engineering can increase predictability and quality in AI-generated simulation code, exceeding production-standard baselines for random number generation, and indicate potential extensibility toward emerging computational paradigms.

Social gaze behavior provides insight for processing social information across primates, including humans. In this study, we investigated this process in non-human primates by analyzing gaze behavior in rhesus monkeys while they viewed videos depicting social interactions among conspecifics. To quantitatively assess how monkeys interpret socially relevant visual information, we developed a simulation-based framework that models the visual information available in each stimulus frame and compares it with observed viewing patterns. As part of this framework, we developed an automated segmentation algorithm as a tool for extracting face and body regions from dynamic social stimuli. Real-viewing fixation data descriptors were extracted from empirical scanpaths, whereas segmentation-derived descriptors were extracted from manual ground-truth segmentation and the automated segmentation algorithm. Descriptors derived from manual segmentation showed high similarity to real-viewing descriptors, indicating strong alignment with observed gaze behavior. However, descriptors derived from the automated segmentation algorithm also showed strong similarity to real-viewing descriptors and closely followed the manual-segmentation descriptor structure, suggesting that the automated algorithm preserved substantial gaze-relevant visual information. Together, these results demonstrate a quantitative, simulation-driven approach for linking social stimulus features with gaze allocation in non-human primates and provide a methodological foundation that may be extended to human social cognition and translational studies of altered gaze behavior.

Spiking neural networks (SNNs), with temporal dynamics and biologically inspired spiking behavior, offer improved interpretability and performance in real-time control. This paper integrates reinforcement learning (RL) with reward-modulated, bio-inspired SNNs that employ reward-modulated spike-timing-dependent plasticity (R-STDP) to address real-time control tasks. The proposed model enables bio-inspired learning by combining conventional spike-timing-dependent plasticity (STDP) with time-dependent, reward-modulated weight updates. Furthermore, it reconstructs the task-oriented state space and reward mechanism to realize a cross-task control framework. Experimental results from control tasks, along with comparisons to traditional RL methods, demonstrate the effectiveness of SNNs in both simulation and real-world applications. This study aims to bridge the gap between SNNs and traditional feedback mechanisms in control systems, highlighting how bio-inspired approaches can enhance adaptive control.

This paper demonstrates a computational framework comparing the emergent behavior of two types of swarm drone systems using Agent-Based Modeling (ABM). The two swarm models are a Leader–Follower (L-F) swarm model, a modified version of Wilensky’s “Ant Lines” model, and a Flocking model based on a simplified Reynolds “Boids” model. The objective of the simulated operation is to deliver a user-defined number of drones of each type to a target area of interest. The resulting visualized product shows how complex behaviors emerge as both models navigate an environment populated with randomly placed obstacles. The research shows that the Flocking swarm is most efficient in over 40,000 simulated cases. However, with more obstacles added to the simulation environment, the L-F model improves its success rate in these cases and is best overall for faster task completion. The results show the potential of using ABM as part of a user’s toolkit for resource allocation and scenario-based decision-making.

Simulating cyber-physical systems (CPSs) often requires integrating continuous dynamics, typically described by differential equations, with discrete-event dynamics that represent the behavior of embedded controllers. Version 3.0 of the functional mock-up interface (FMI) standard introduces synchronous clocks and a discrete-event mode, creating new opportunities for the co-simulation of hybrid systems. So far, it has not been demonstrated that these features can faithfully capture the semantics of discrete-event formalisms such as the discrete-event system specification (DEVS) and its extension, parallel DEVS (PDEVS). This leaves open the question of whether FMI 3.0 can effectively support complex hybrid dynamics. This paper addresses this gap by showing how PDEVS models can be exported and coupled as FMI 3.0 functional mock-up units (FMUs) while preserving PDEVS semantics. We first derive requirements that any simulator must satisfy to correctly preserve PDEVS semantics. Based on these requirements, we present a systematic mapping of PDEVS abstract simulator functions to the FMI 3.0 interface with synchronous clocks and present an orchestration algorithm to co-simulate multiple PDEVS FMUs accordingly. The approach is validated using an adaptive cruise control case study, where our FMI-based co-simulation reproduces the results of an established PDEVS simulator. To support reproducibility and further research, we provide an open-source prototype together with the complete validation setup.

In recent years, terrorist attacks all over the world caused many civilian casualties seriously. Under the knife and axe terrorist attack, there are usually four groups of event-related persons, that is, pedestrians, terrorists, police, and safety guards in the scene. The behavior of them is different, but they are rarely considered together in existing litterateurs, especially the police and safety guards. In this paper, we propose the FFPS (extended floor-field model combined with on-site control actions of police and safety guards) model to study pedestrian evacuation dynamics considering terrorist attack on-site control action differences between police and security guards. The FFPS model consists of three aspects. First, pedestrian evacuation dynamics in terrorist attack environment is modeled. Second, the behavior of terrorists, police and safety guards is studied. Third, the differences of on-site control actions between police and safety guards are studied. We set up the scenario for simulation. The influence of terrorists’ number and location, police and security guards’ different actions, police entering time, police shooting distance, police and safety guards’ spatial position, and police control strategy on evacuation dynamics are analyzed in detail. Those results provide valuable insights to quickly control terrorists, and sharply reduce casualties in terrorist attack environment.

As digitalization is permeating all sectors of society toward the concept of “smart everything,” and virtual technologies and data are gaining a dominant place in the engineering and control of intelligent systems, the Digital Twin (DT) concept has surfaced as one of the top technologies to adopt. This paper discusses the DT concept from the viewpoint of Modeling and Simulation (M&S) experts. It both provides literature review elements and adopts a commentary-driven approach. We first examine the DT from a historical perspective, tracing the historical development of M&S from its roots in computational experiments to its applications in various fields and the birth of DT-related and allied concepts. We then approach DTs as an evolution of M&S, acknowledging the overlap in these different concepts. We also look at the M&S workflow and its evolution toward a DT workflow from a software engineering perspective, highlighting significant changes. Finally, we look at new challenges and requirements DTs entail, potentially leading to a revolutionary shift in M&S practices. In this way, we hope to foster the discussion on DTs and provide the M&S expert with innovative perspectives.

The macaw palm (Acrocomia aculeata) is a palm tree native to tropical forests that stand out due to its great potential for oil production. This study was developed with the objective of constructing a high-fidelity model of the macaw palm fruit-rachilla system for the purpose of simulating its dynamic behavior when subjected to mechanical vibrations. The finite element method was used to determine the natural frequencies and modes of vibration of the system. The three-dimensional models of the fruit-rachilla systems were elaborated using CAD3D Fusion 360 software. The modal properties of the fruit-rachilla systems were obtained based on the models developed by varying the elasticity modulus values of the system. The parameters of greatest influence in the estimation of natural frequencies are the elasticity modulus, especially that of the fruit-rachilla joint, and the specific mass. The models that take into account the three-dimensional strains along the rachilla are the least sensitive to variations in the mechanical properties (elasticity modulus and specific mass) and are shown to be more representative of the actual physical system.