Simulation

Hargis B.E., Papelis Y.E.

This work presents a hybrid scenario synthesis approach combining static and adaptive techniques to evaluate autonomous maritime systems under multi-factor conditions. Static scenarios offer efficient, expert-driven testing but can falter in complex environments where assumptions no longer hold. Adaptive scenarios capture system adaptability through evolving interactions but demand greater design and computational resources. The hybrid method deterministically generates maritime traffic interactions, supporting black-box evaluations resilient to unpredictable system motion. Key innovations include precise interaction design, closed-loop scenario selection based on prior system behavior, and efficient filtering of test cases. The method is implemented within the U.S. Navy’s Autonomous Systems Test Capability, specifically leveraging the Virtual Maritime Testing Environment which supports high-fidelity, multi-agent simulations with runtime triggers and intelligent traffic behaviors that enable repeatable, goal-driven testing scenarios. Verification through MATLAB-based implementation and Monte Carlo simulations showed consistent scenario execution. These results, when compared to static scenario analysis, demonstrate the approach’s robustness.

Du W., Zheng R., Yan R.

Early detection and diagnosis of traumatic brain injury (TBI) are vital for accurate prognosis. Magnetic resonance imaging (MRI) and computed tomography (CT) are the main diagnostic tools in clinics, but their high cost, large size, and lengthy detection times drive researchers to seek new methods for identifying brain injuries. Many researchers detect cerebral hemorrhage by analyzing electromagnetic variations in brain regions using electromagnetic induction. Initial feasibility assessments of this method should be validated through simulation experiments that include developing a brain model. The development of the model requires careful consideration of both simplicity and complexity. An overly simplistic model may compromise the accuracy of simulation results, while an excessively complex model can lead to increased computational time and higher hardware specifications. This paper presents a novel method for constructing a four-layer brain model that addresses these concerns effectively. The model comprises the scalp, skull, spinal fluid, and brain. This approach enhances the accuracy and authenticity of the model and optimizes computational efficiency.

Lazarova-Molnar S.

Elbellili E., Blondeel P., Huybrechs D., Lauwens B.

The growing intricacy of contemporary engineering systems, typically reduced to differential equations, poses a difficulty in digitally simulating them. The linearly implicit quantized state system (LIQSS) provides a different method from traditional numerical integration techniques for tackling such problems. This method is effective in large sparse stiff systems and systems with frequent discontinuities. However, this method could be further improved. First, the algorithm can step through the solution analytically or through iterations. A comparison is presented in this article. Second, the intrinsic discrete behavior of this new method can cause oscillations that lead to small unnecessary simulation steps. A prior approach was made to detect and terminate these cycles. Different detection mechanisms are examined in this article. Third, a linear approximation was used. Its enhancement is also investigated in this work. Finally, the article provides which of these modifications improved the overall performance of some systems simulations using LIQSS order one.

Mikram H., El Kafhali S.

The growing demand for cloud computing services has led to a rapid expansion of cloud data centers (CDCs), significantly increasing global energy consumption, driven by underutilized physical machines and continuous cooling overhead. To address these challenges, cloud providers are adopting green solutions such as dynamic consolidation, which minimizes the number of active physical machines while maintaining system performance. In this comparative study, we model task arrivals using five probability distributions (Normal, Lévy, Pareto, Chi-square, and Binomial) to explore their impact on the scheduling efficiency of six well-established metaheuristic algorithms; genetic algorithm (GA), ant colony optimization (ACO), cuckoo search (CS), particle swarm optimization (PSO), artificial bee colony (ABC), and simulated annealing (SA). By introducing probabilistic variation in task arrival times, the study examines the sensitivity of these algorithms to dynamic workloads. Using the CloudSim simulator, performance is assessed across small- and large-scale CDC environments based on makespan, energy consumption, and resource utilization. Results reveal that Pareto-distributed task arrivals yield consistently strong performance in small-scale scenarios, while PSO paired with chi-square distributions outperforms others in large-scale settings.

Zadeh S.A., Zandieh M., Alem Tabriz A.

Efficient inventory management of blood and its components is a highly important problem in blood supply chain management (BSCM). It ensures that lives are not endangered due to insufficient supply while also minimizing wastage. Given the inherent uncertainty in supply and demand within the blood supply chain (BSC), effective inventory management depends on balancing these two factors. A key research gap in this area is the application of real-time information sharing to balance supply and demand through targeted donation—where donations are made based on actual system needs, preventing unnecessary contributions. This paper has two main objectives: (1) To systematically analyze how the interrelationships among influencing factors shape the behavior of the BSC concerning three key performance indicators (KPIs)—wastage rate, fill rate, and inventory level, and (2) To introduce, for the first time in the literature, a cloud-based BSCM (CBSCM) system as a proposed e-healthcare improvement policy. We employ a system dynamics (SD) approach to examine the structure of the BSC. Within the SD methodology, we illustrate reference modes, identify key influencing variables, develop causal loop and stock-and-flow diagrams, run simulations using available data, and validate the results. To assess the model’s real-world applicability, we conduct a case study on Iran’s BSC. The findings highlight the superiority of CBSCM over the conventional system from the perspective of the considered KPIs. Therefore, CBSCM can serve as an effective policy for countries aiming to implement e-BSCM.

Gumahad B., Collins A.J.

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.

González A., Cristiá M., Luna C.

Real-time DEVS (RT-DEVS) can model systems with quantitative temporal requirements. Ensuring that such models verify that kind of temporal properties requires to use something beyond simulation. In this work, we use the model checker Uppaal to verify a class of recurrent quantitative temporal properties appearing in RT-DEVS models, even though Uppaal cannot deal in general with this kind of properties. In order to overcome these limitations, we use the technique known as automata observer. Second, by introducing mutations to quantitative temporal properties, we are able to find errors in RT-DEVS models and their implementations. A case study from the railway domain is presented.

Friederich J., Khodadadi A., Lazarova-Molnar S.

Stochastic Petri Nets (SPNs) are a powerful formalism, widely used for modeling complex systems in various domains, ranging from manufacturing and logistics to healthcare and computer networks. In this paper, we introduce PySPN , a flexible and easily extendable Python library for Modeling and Simulation of SPNs. Besides the simulation of SPNs, we further extended PySPN with the functionality of generating synthetic data in the form of event logs from SPNs’ simulations. Event logs in simulation models are essential for ensuring model accuracy, evaluating performance, debugging, and facilitating decision-making processes. Event logs offer a comprehensive record of simulated events, which can be analyzed to gain insights into systems’ behaviors and performance. PySPN aims to provide researchers, engineers, and simulation practitioners with a user-friendly and efficient toolset to model, simulate, and analyze SPNs, facilitating the understanding and optimization of stochastic processes in dynamic systems.

Dias C.A., Landre Júnior J.

Driver-in-the-loop (DIL) simulation has become crucial in the automotive industry, providing a controlled setting for assessing vehicle performance, driver characteristics, and scenario configurations. However, the incorporation of human factors introduces subjectivity into simulation outcomes. This systematic review examines the interplay between objective metrics (OM) and subjective assessments (SA) in DIL vehicle dynamic simulator research. To achieve this, PubMed and ScienceDirect databases and predefined keywords and boundary conditions are used. Through four eliminatory revision stages, most of the ultimately selected papers are scrutinized to determine if a viable methodology exists for addressing subjectivity in DIL simulations. The results indicate that most studies found a correlation between the OM and the SA. Another positive aspect supporting this conclusion is that most works are associated with the fidelity of virtual tests in vehicle simulators. Despite the initially positive findings, it is noteworthy that most studies utilized a mix of standardized questionnaires and custom surveys, highlighting both the challenge of relating works due to a lack of standardization and the need for caution regarding the implicit subjectivity in questionnaire creation for each research. In addition, some secondary results are discussed based on the metadata gathered.

Anderson T., Shepherd P.

Negahban A.

This paper investigates the general problem of comparing multidimensional simulation output with a given data set (e.g., real-world historical data). This problem frequently arises in verification, validation, and calibration of simulation models with spatial output statistics as in weather/climate, epidemic, swarm/crowd, social systems, communication networks, and many other applications where the simulation output is distributed across various locations or geographical regions. In the case of univariate simulation output, two-sample statistical hypothesis tests such as the t -test are commonly used. For simulation models with multidimensional and spatial output statistics, the Hotelling’s two-sample test is widely used as the benchmark method in the simulation literature. However, the Hotelling’s test assumes that the two samples come from multivariate Gaussian distributions with equal covariance matrices, which may not be the case in many applications. To address this gap, this paper proposes a double-bootstrap method based on the Wasserstein distance for comparing two multidimensional samples. Unlike the Hotelling’s test and other parametric approaches, the proposed method does not require restrictive distributional assumptions, enabling a wider range of applications and contributing to verification, validation, and calibration of simulation models with multidimensional output. Computational experiments are performed to assess the test power, and the results indicate that the proposed method outperforms the Hotelling’s test and various other approaches. The proposed method’s applicability is illustrated through two examples related to random walk of swarm particles on a two-dimensional space and a realistic engineering application involving simulation of unmanned aerial vehicle (UAV) communication systems.

Staff M.E., Mustafee N.

Perishable products are associated with a limited shelf-life, and their efficient management often requires close matching of supply with demand. Due to the inherent uncertainty in supply chains, determining stock reordering points and issuance policies is challenging. Tools and techniques from Operations Research/Management Science (OR/MS) support decision-makers in making well-informed decisions related to perishable inventory management. Among the plethora of OR/MS methods, discrete-event simulation (DES) is well suited for studying inventory systems, as this typically models products moving in and out of storage within a stochastic supply chain environment, and in the case of perishable goods, enabling age tracking of products. This paper presents a literature review of DES applied to perishable inventory management. Our base set of literature consists of 25 papers retrieved through searches of scholarly databases. Notably, our review highlights that fields such as the pharmaceutical, organ donation, and floral and horticultural supply chains are relatively underexplored. Furthermore, while most modeling studies consider uncertainty on the demand side, uncertainties related to lead time, yield, or product lifetime have not been modeled to a great extent. Our review is a key source of literature for researchers and practitioners on the current state-of-the-art in DES modeling for perishable inventory; it identifies research gaps and provides directions for future research.

Li J., Si L., Chen M., Wang Z., Wei D., Gu J.

The automation of top coal caving is crucial for advancing unmanned coal mining technologies. During the top coal caving process, a coal-gangue mixture—comprising coal, gangue, and air—forms at the tail beam of hydraulic supports. This mixture exhibits diverse electromagnetic parameters, volumes, and shapes. This study investigates the propagation characteristics of electromagnetic waves within a coal-gangue mixture model and examines how varying operational conditions affect wave propagation. A novel three-dimensional reconstruction method based on multi-view imaging is introduced to accurately capture the geometric characteristics of coal and gangue blocks. Furthermore, a firefly optimization algorithm is enhanced to develop a random medium model that effectively simulates the spatial distribution and electromagnetic properties of coal-gangue mixtures. Results from CST simulations reveal significant insights into the propagation behavior of electromagnetic waves under differing dielectric constants, conductivities, and moisture contents. These findings underscore the potential for improving coal-gangue identification techniques in automated mining operations.

Khalid R., Mohd Nawawi M.K., Ramli R., Ishak N., Sakari N.F.

One technique to measure system performance is using discrete event simulation (DES), which models organizational structures and behavior. DES also allows testing of resource configurations to assess their impact on performance. To evaluate their efficiency, data envelopment analysis (DEA) can be used. However, current DES software does not automate DEA for efficiency evaluation, requiring separate analysis of performance measures and resource efficiency. This complicates finding the most efficient resource configuration, especially in healthcare systems. To address this, this paper proposes a framework combining DES and DEA for simpler analysis of their inputs and outputs. The framework automates data transfer mechanisms between DES outputs and DEA inputs and implements an integrated computational approach to DEA. To validate the framework, a case study in an emergency department was conducted, where complex interconnected processes are common, and optimizing resource allocation is critical for patient care and system performance. The case study analyzed 35 resource configurations to identify the most efficient one. The results demonstrated the framework’s potential to simplify resource analysis, identify optimal configurations, and enhance decision-making, thereby improving the system’s operational efficiency. The framework provides a robust, portable, and scalable solution applicable across diverse industries for effectively optimizing system performance and resource allocation.