This paper discusses two modeling and simulation (M&S) issues for defense cyber-physical systems (CPSs): 1) model-driven development via model interoperation and 2) simulation analysis between interoperated models. To clearly describe the network capabilities between the CPSs within network-centric warfare (NCW), we first modeled communication factors separately from the overall CPSs. The CPS models and the network model were considered discrete event systems, which were realized with DEVSim++ and ns-3, respectively. For simulation of the two types of models, we interoperated them with high-level architecture-based middleware to preserve the independence of each one. The goal for NCW analysis is to find a balance between the computational, physical, and communication factors of the defense CPSs to achieve their missions within a networked environment. In the case study of network-centric ground warfare, the CPS models generate mission effectiveness to be analyzed according to realistic network conditions; at the same time, the network model enables the analysis of the network performance given validated combat scenarios. We expect this study will help simulation experts interactively analyze computational and physical capabilities of the CPSs as well as communication effects between the CPSs.

This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.

The optimization of overhead hoist transport (OHT) systems in semiconductor manufacturing plays a crucial role in improving production efficiency. In this study, the development of a discrete event simulation model to analyze the physical and control characteristics of an OHT system is presented, focusing on building a modular simulation framework for evaluating operational strategies by applying various optimization techniques. Additionally, a step-by-step analysis is introduced to optimize OHT operation using the developed model. The simulation model is broadly divided into three parts according to their purposes. The physical system encompasses the physical entities such as the equipment and vehicles. The experimental frame comprises a generator, which triggers experiments, and a result analyzer. Finally, the system controller is structured hierarchically and consists of an upper layer, known as the manufacturing control system, and subordinate layers. The subordinate layers are modularly divided according to their roles and encompass a main controller responsible for OHT control and a scheduling agent manager for dispatching and routing based on SEMI commands. The proposed simulation model adopts a structure based on the discrete event systems specification (DEVS). Since the hierarchical system controller may face challenges such as computational overhead and adaptability issues in real-world implementation, the modular design based on DEVS is utilized to maintain independence between layers while ensuring a flexible system configuration. Through an exploratory analysis using the simulation model, we adopt a step-by-step approach to optimize the OHT operation. The optimal operation is achieved by identifying the optimal number of OHT units and pieces of equipment per manufacturing zone. The results of the exploratory analysis for the three scenarios validate the effectiveness of the proposed framework. Increasing the number of OHT units beyond 17 resulted in only a 0.08% reduction in lead time, confirming that 17 units is the optimal number. Additionally, by adjusting the amount of equipment based on their utilization rates, we found that reducing the amount of equipment from 12 to five in process E-1 and from seven to three in the OUT process did not degrade performance. The proposed simulation framework was thus validated as being effective in evaluating OHT operational efficiency and useful for analyzing key performance indicators such as OHT utilization rates. The proposed model and analysis method effectively model and optimize OHT systems in semiconductor manufacturing, contributing to improved production efficiency and reduced operational costs. Furthermore, this work can bridge the gap between theoretical modeling and practical complexities in semiconductor logistics.

Communication system in the network‐centric warfare (NCW) has been analyzed from the perspective of the system of systems (SoS), which consists of a combat system and a network system so that the two reflect each other’s effects. However, this paradoxically causes a prolonged execution time. To solve this problem, this paper proposes an advanced integrated modeling method for the communication analysis in the NCW via the transformation of the SoS, which reduces the simulation execution time while ensuring the accuracy of the communication effects. The proposed models mainly cover interentity traffic and intraentity mobility developed in the form of feed‐forward neural networks to guarantee two‐way interactions between the combat system and the network system. Because they are characterized as discrete events, the proposed models are designed with the discrete‐event system specification (DEVS) formalism. The experimental results show that the proposed transformation reduced an error by 6.40% compared to the existing method and reduced the execution time 3.78‐fold compared to the SoS‐based NCW simulation.

The need for certification standards for new convergence products, such as a smart LED streetlight system, has been identified as a critical issue. This study proposes simulation modeling for smart LED streetlight systems and suggests three certification standards: the minimum time to initiate dimming-up, the duration of the dimming-up period, and the number of concurrently controlled streetlights. We utilized Relux to model streetlights and roads in terms of luminance levels, and used analytical formulas to compute the braking distances of oncoming vehicles. The two models were integrated into a smart LED streetlight system model using Simio. Simulation experiments were conducted with two objectives: to provide certification standards, and to apply and verify them in real-world cases. We experimented with 630 scenarios, modeling various dynamic situations involving roads and vehicles, and applied the model to two actual roads in the Republic of Korea to test its validity. The model was subsequently applied to roads for which traffic-volume data were available, to determine potential energy savings. The proposed simulation method can be applied to a smart LED streetlight system and to new products that lack certification standards. Furthermore, the proposed certification standards offer alternative approaches to operating streetlight systems more efficiently.

Simulation of a system‐of‐systems (SoS) model, which consists of a combat model and a network model, has been used to analyze the performance of network‐centric warfare in detail. However, finding the combat model parameters satisfying the required combat power using simulation can take a long time for two reasons: (1) the prolonged execution time per simulation run and (2) the enormous number of simulation runs. This paper proposes a simulation‐based optimization method for the SoS‐based simulation model to overcome these problems. The method consists of two processes: (1) the transformation of the SoS‐based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. The experimental result reveals that the proposed method significantly reduced the time for finding the optimal combat parameters with an acceptable level of accuracy.