Aerodynamic drag is a dominant factor affecting the efficiency and driving range of electric trucks, especially at high speeds. Although previous studies have mainly focused on external drag-reduction devices, internal airflow control strategies such as integrated ducts have received limited attention. This study addresses that gap by analyzing how placing an internal duct in the truck body affects drag and power efficiency. A simplified two-dimensional computational fluid dynamics (CFD) approach was employed using ANSYS Fluent to evaluate and compare a baseline truck model and a modified configuration featuring a central internal duct. The simulations assessed the drag coefficient, drag force, propulsion power, and energy consumption over a wide range of inlet velocities (50–140 km/h). The results show that the ducted configuration consistently outperforms the baseline model, achieving up to a 7.5% reduction in drag force, 7.53% savings in energy consumption per kilometer, and an 8.2% extension in driving range at 140 km/h. These findings confirm the aerodynamic and energy-saving potential of internal ducting as a passive design strategy for heavy-duty electric vehicles.

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.

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.

This paper introduces a novel simulation modeling framework that seamlessly integrates Petri nets with logic programming, resulting in intelligent and adaptable simulation models capable of handling both discrete and continuous events. Instead of treating systems as standalone Petri nets, this methodology embeds Petri net semantics within simulation models, creating a hybrid state vector that incorporates attributes from both the Petri net and model-specific states. In addition, the modular design of the model supports high reusability and scalability, making it applicable across various domains, including industrial automation and workflow management. Embedding Petri nets within simulation entities enhances dynamic decision-making and specialized time-management capabilities. This approach reduces errors associated with fixed time-step sizes, thereby improving the accuracy of continuous event simulations. The solution introduces the external transition concept for Petri nets and implements a time-management approach based on qualitative landmarks and mathematically meaningful points, effectively minimizing errors caused by simulation time-step variations. To demonstrate the framework’s versatility and effectiveness, six example applications illustrate how combining Petri nets with logic programming enhances the analytical power and flexibility of traditional simulation models. Ultimately, this paper establishes a comprehensive hybrid simulation framework that unifies discrete and continuous analysis, endows simulation entities with reasoning capabilities through logic programming, and provides robust modularity.

With the increasing adoption of self-service bag drop facilities, modern airports necessitate check-in counter optimization models that strategically allocate passengers between staffed and self-service facilities while balancing operational costs and service quality. This study develops a two-tiered decision framework integrating dynamic integer programming and discrete-event simulation (DES) to minimize manual counter-staffing costs while guaranteeing service level agreements. We first formulate an integer programming model that allocates passengers to manual or self-service channels based on passengers’ profiles. To address queue overflow during peak hours, a dynamic capacity scaling factor is introduced to the integer programming model. The DES module then iteratively validates queueing dynamics and feeds back queueing time to optimization layer, triggering increase in the capacity scaling factor and reallocations when predicted wait times exceed 15-min thresholds. Applied to Baiyun Airport Terminal 2, the model results show a 9.2% increase in operational cost (from 8824.5 to 9633.75) but a 30.9% reduction in average manual check-in wait time (from 8.1 to 5.6 min), reducing peak-period congestion. This study provides decision support for optimizing check-in counter operations at airports.

The Integrated Stribeck Friction Model (ISFM) is introduced and compared to the Dahl and LuGre friction models. The Dahl model achieves correct steady-state Coulomb friction but exhibits excess creep in its transient response. The LuGre model can represent an arbitrary Stribeck friction curve, yet also exhibits excess creep in its transient response and displays nonpassive behavior during velocity reversals. The ISFM represents an arbitrary Stribeck curve with a more realistic friction force and creep rate formulation. As a result, it achieves correct steady-state friction, and a realistic transient creep response while remaining passive under all conditions. A basic four-parameter representation of a Stribeck curve is provided for use with the ISFM, which is competitive with the representation typically used with the LuGre model. Unlike the Dahl and LuGre models, the ISFM is designed for compatibility with scenarios where the friction surfaces might lose contact with each other. A literature review indicates it is the simplest existing stateful friction model that remains well defined when contact loads are zero.

Complex sociotechnical systems with multiple competing objectives and nonlinear dynamics pose significant challenges for policy optimization. Traditional simulation-based methods often struggle with high-dimensional policy spaces. This study addresses these challenges by combining genetic algorithms (GAs) with system dynamics (SD) modeling to optimize policy configurations in complex environments. Our approach merges SD’s capacity to simulate intricate system behaviors with GA’s prowess in multi-objective optimization. The SD model uses predefined decision variables to simulate system behavior, while GA iteratively adjusts these variables to find optimal policy configurations. We apply this hybrid approach to a media industry case study, focusing on balancing profit, competitiveness, and audience satisfaction. The research methodology integrates SD’s ability to capture complex system behaviors with GA’s strength in optimizing multiple objectives. An SD model simulates system behavior based on predefined decision variables representing key policy levers. GA then iteratively adjusts these variables based on fitness objectives derived from the SD model. This process evaluates performance and identifies the globally optimal policy configurations. The results show that the hybrid SD–GA framework significantly improves policy solutions compared to conventional methods. Sensitivity analysis confirms the optimized policies’ robustness and comparative assessments highlight our approach’s advantages in navigating complex policy spaces. This study introduces a new SD–GA framework that improves data-driven policy formulation in complex systems. It combines policy informatics and evolutionary algorithms to offer a comprehensive approach to multi-criteria decision-making. Future research could include long-term validation of this approach in the broadcasting industry to further refine and apply this methodology across various domains.

This paper documents the origins and development of the parallel and distributed simulation field, especially as it relates to parallel discrete event simulation (PDES) technology. My focus is on the people and ideas that contributed to the creation and evolution of PDES technology over the past five decades. Much of this paper reviews the research conducted in academia and research laboratories that were the driving forces behind the development of PDES technology as well as subsequent efforts in government and industry to bring the technology to modeling and simulation practitioners. The origins of PDES technology lie in a fundamental technical issue called the synchronization problem. This problem is discussed, as well as the two main solution approaches that were created. The development of these approaches and their evolution to form the time management services of the High Level Architecture (HLA) standard are described, as well as the community of researchers and practitioners that formed in establishing this field. Commercialization efforts are discussed followed by a brief discussion of areas requiring further research. The importance of the Society for Modeling and Simulation International (SCS) and its flagship journal, Simulation, in the development of the field are discussed. Throughout this discussion, I provide my personal observations and take away lessons concerning the development of the field.

Business-to-Consumer (B2C) e-commerce has become a dominant, continuously evolving force in global retail. Most consumers today make cross-border purchases in marketplaces, while enterprises have found great opportunities, and competition has become increasingly fierce. To understand B2C properties is an important endeavor. This paper achieves two goals: it develops a method to prove tendencies in a simulation and shows the oligopolistic nature of cross-border B2C e-commerce marketplaces. These achievements enhance the understanding of cross-border B2C e-commerce, by employing novel approaches: Social simulation, agent-based modeling (ABM) tools, theorem-proving techniques, and scenario analysis. The proving method began with an experimental design to explore the model’s dynamics for parameters defined for specific scenarios of interest, and the agents’ options were randomly selected for a significant number of runs (Monte Carlo experiment). This procedure allowed us to identify and prove the necessity of a tendency (the oligopolistic character of cross-border B2C e-commerce) and determine the factors that drive it. In the second part of the proof procedure, the persistence, independence of the agents’ choices, and scaling validity of the tendency were shown by significantly increasing the number of random experiments and the number of simulated agents. The model was also validated. The developed method satisfactorily addresses some challenges of theorem-proving. In all these experiments, the variable of interest was market share. The resulting order of influence of the factors driving market share was recognition, product and service attributes of the marketplaces, and word of mouth. Surprisingly, word of mouth was the least important factor.