SIMULATION: Transactions of The Society for Modeling and Simulation International

Khalid, Ruzelan; Mohd Nawawi, Mohd Kamal; Ramli, Razamin; Ishak, Nurhanis; Sakari, Nur Fatihah

doi: 10.1177/00375497251337090pmid: N/A

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.

Li, Jiahao; Si, Lei; Chen, Miao; Wang, Zhongbin; Wei, Dong; Gu, Jinheng

doi: 10.1177/00375497251337726pmid: N/A

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.

Negahban, Ashkan

doi: 10.1177/00375497251337721pmid: N/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.

González, Ariel; Cristiá, Maximiliano; Luna, Carlos

doi: 10.1177/00375497251340070pmid: N/A

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.

Dias, Cádmo Augusto Rodrigues; Landre Júnior, Jánes

doi: 10.1177/00375497251341905pmid: N/A

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.