Simulation Case Study Research Articles

An EM algorithm for fitting matrix-variate normal distributions on interval-censored and missing data

Matrix-variate distributions are powerful tools for modeling three-way datasets that often arise in longitudinal and multidimensional spatio-temporal studies. However, observations in these datasets can be missing or subject to some detection limits because of the restriction of the experimental apparatus. Here, we develop an efficient EM-type algorithm for maximum likelihood estimation of parameters, in the context of interval-censored and/or missing data, utilizing the matrix-variate normal distribution. This algorithm provides closed-form expressions that rely on truncated moments, offering a reliable approach to parameter estimation under these conditions. Results obtained from the analysis of both simulated data and real case studies concerning water quality monitoring are reported to demonstrate the effectiveness of the proposed method.

Simulation Evaluation and Case Study Verification of Equipment System of Systems Support Effectiveness

As the scale of missions continues to expand, equipment support has emerged as a critical component of military combat effectiveness. Consequently, the supportability of a system of systems (SOS) for equipment has become as essential quality requirement alongside its performance metrics. This study systematically assessed the effectiveness of equipment SOS support through a task-driven methodology. Initially, a model for generating equipment support tasks was developed to translate the operational requirements into a sequence of support tasks. Subsequently, a simulation model was constructed to evaluate the equipment SOS support system, and solutions were derived for the corresponding SOS-level support effectiveness indexes. Finally, the feasibility and characteristics of the proposed models and simulation methodology were validated through a case study involving an emergency operational mission for an air combat group formation. The results indicate that the increased reliability of the equipment system correlates with a reduced failure rate and lower resource consumption for maintenance and support per device, thereby improving support efficiency. The methodology presented in this article provides a framework for evaluating the effectiveness of equipment SOS support while facilitating informed decision-making in information warfare conditions.

Weak Fault Feature Extraction in Non-Gaussian Noise Interference Based on Adaptive Recombination Empirical Wavelet Transform Incorporated by Sparse Coding Shrinkage

Abstract The fast kurtogram (FK) is a very useful tool in the field of fault diagnosis, but it also contains two drawbacks. On one hand, its kurtosis indicator is susceptible to random impulse interference, leading to frequency band mis-selection. On the other hand, the frequency band division method determined by the tree filter bank may cause under or over decomposition problems. Therefore, in this paper a novel fault diagnosis theory is constructed combining the adaptive recombination empirical wavelet transform (AREWT) with the envelope spectral energy ratio (ESER). Adaptive decomposition of frequency bands is first performed using AREWT. Afterwards, the ESER is proposed as a statistical indicator to choose optimal demodulation frequency band which overcomes the effects of non-Gaussian noise interference and improves diagnostic accuracy. To further highlight the fault elements of the selected components, the adaptive sparse coding shrinkage algorithm (ASCS) is introduced to sparsely denoising sensitive components. Correspondingly, clear fault feature frequency components could be extracted from envelope spectrum. Finally, the practicability and superiority of proposed AREWT-ESER approach were fully validated through numerical simulation signal and case studies.

Quantifying household vulnerability to power outages: Assessing risks of rapid electrification in smart cities

As cities accelerate decarbonization through building electrification, the growing dependence on electrical systems introduces new vulnerabilities during power disruptions. While grid-level resilience has been widely studied, household-scale impacts of electrification remain poorly understood. In this study, we develop a vulnerability assessment framework that combines machine learning classification with high-resolution synthetic energy data from 129,000 U.S. single-family homes. Our two-stage approach first identifies household electrification levels with over 80% accuracy and then quantifies outage vulnerability using a composite risk index that incorporates electrification profiles, backup capabilities, and climate exposure. A simulated case study reveals that fully electrified households face significantly higher risks during winter storms, with a 60% greater vulnerability compared to mixed-energy homes. In contrast, solar-equipped electrified households exhibit enhanced resilience during heat waves, leveraging renewable energy resources to mitigate risks. By highlighting critical dependencies and adaptive capacities, our framework emphasizes the importance of energy diversity and distributed energy resources in reducing outage vulnerabilities. This scalable, non-intrusive methodology provides actionable insights for policymakers and urban planners to design climate-resilient urban energy systems.

Research on flexible weaving planning based on NSGA-II algorithm

Facing the shift in the weaving industry from mass production to a more diversified, small-batch production model, traditional production scheduling systems are no longer capable of meeting the demand for rapid market response. To address this issue, this paper first analyzes the production process of a weaving workshop, identifying key scheduling challenges such as order allocation, equipment selection, and operation sequencing. Based on this analysis, a flexible job shop multi-objective scheduling model tailored for weaving workshops is developed. To handle the multiple constraints and optimization goals inherent in the model, an improved NSGA-II algorithm is proposed. This algorithm combines artificial bee colony (ABC) algorithm for population initialization with simulated annealing (SA) for population filtering. Simulation examples and case studies from actual workshops demonstrate that the improved NSGA-II algorithm outperforms other algorithms in solving the scheduling problem for weaving workshops. The proposed multi-objective scheduling model and its improved algorithm provide accurate and efficient optimization solutions for workshop scheduling.

Predefined-time adaptive neural network decentralized control for large-scale interconnected systems with input hysteresis.

This study endeavors to develop a predefined-time adaptive neural network decentralized controller for large-scale interconnected nonlinear systems with input hysteresis. Within the framework of the backstepping technique, the proposed control scheme guarantees that the tracking error converges to a small bounded set within a predefined settling time. The upper limit of this convergence time is determined by a single adjustable control parameter. Modified command filter not only tackles the inherent "complexity explosion" issue in traditional backstepping methods but also effectively avoids chattering phenomena possibly induced by sign function. An online approximator based on neural networks is utilized to address system uncertainties. Moreover, a novel predefined-time error compensation mechanism is constructed to compensate for the reduction in control accuracy caused by filtering errors. Two simulation case studies demonstrate the feasibility and effectiveness of the proposed control method.

Detecting environmental trends to rethink soybean variety testing programs

AbstractVariety testing programs (VTPs) use multi‐environment trials (MET) to evaluate and report the performance of commercially available and pre‐commercial soybean (Glycine max L. Merr.) varieties targeting a specific set of environments. Adequate modeling of the environmental variability and genotype–environment interactions (G × E) within the VTP would help farmers and seed companies decide which variety to choose or recommend. We propose an approach to characterize environments using the soybean data from the University of Missouri VTP. We modeled an environmental trend (EnvT) based on the phenotypic mean performance and the observed phenotype in each environment. The environments were classified into four different EnvT environment types, and soil and climate data were used as predictors of the EnvT through eXtreme Gradient Boosting (XGBoost) model. Temperature on late vegetative and flowering, soil‐saturated hydraulic conductivity, and silt content were key drivers of EnvT. The approach identified overrepresented environments (62%) and increased the ratio between variety and G × E variance. A simulation case study verified that the random removal of overrepresented sites from the dataset quickly degraded G × E analysis, implying that increasing the number of underrepresented sites is recommended. Our results demonstrate that environmental characterization is essential for optimizing resource allocation within VTP, thereby supporting the end goal of aiding farmers to utilize the best varieties for their production environment.

Une étude de modélisation et simulation (M&S) du trafic urbain de la ville Bamako s’appuyant sur l’approche de modélisation multi-perspective et simulation holistique (MPM&HS)

Urban traffic occupies a central place in our daily life of a city and gains complexity due to its increasing interdependence with other socio-economic dimensions (such as climate, demography and environmental policies, to name a few). This work presents a modeling and simulation study based on the Multi-Perspective Modeling and Holistic Simulation (MPM&HS) framework that can address such complexity. It is based on the holistic integration of models that have been independently constructed from the respective perspectives of diverse but interdependent dimensions. The resulting model can answer questions that any of the perspective-specific models cannot answer in isolation. We use a traffic modeling and simulation case study to illustrate the effectiveness of the MPM&HS approach. Keywords: Modeling and Simulation (M&S), Multi-Perspective Modeling and Holistic Simulation (MPM&HS), High-Level Language for System Specification (HiLLS), Transportation system, Urban traffic, Anylogic.

Bivariate density estimation under marginal unimodality constraints

Empirical analyses of observed pairs of variables often suggest that the underlying joint distribution of these variables is highly dependent and each one of them is marginally unimodal. Exploiting marginal shape information can improve the bivariate density estimation. We develop a nonparametric density estimation method where the joint density of the bivariate outcome variables is estimated using a flexible class of mixtures of scaled Beta distributions and the mixing weights are suitably constrained to ensure marginal unimodality. Large sample consistency of the proposed density estimator is established using the method of sieves and Argmax Continuous Mapping Theorem. The proposed method enlarges the scope of previous studies in two important aspects: (i) joint density provides a simultaneous estimation of higher-order moments beyond mean and variance functions, and (ii) marginal shape constraints provide more efficient estimates. The proposed procedures are illustrated using simulated and real case study datasets on gestational age and infant birth weight.

Risk Budgeting Allocation for Dynamic Risk Measures

Time-Consistent Diversified Portfolio Allocations Portfolio allocation problems typically involve diversification of risks, which can be achieved by risk budgeting strategies. Of additional importance is being time consistent—that is, ensuring that decisions about what to do in the future under certain outcomes remain optimal when those future outcomes are realized. In “Risk Budgeting Allocation for Dynamic Risk Measures,” Pesenti, Jaimungal, Saporito, and Targino develop time-consistent portfolio strategies, termed dynamic risk budgeting strategies, where the diversification is on the level of risk contribution of assets. The authors formalize the dynamic setting of risk budgeting strategies, recast dynamic risk budgeting strategies as solutions of sequences of convex optimization problems, and develop an efficient actor-critic deep reinforcement learning algorithm for estimating dynamic risk budgeting strategies. The algorithm and the dynamic risk budgeting strategies are illustrated on a complex simulation case study involving five assets and 12 time steps.

Regional-scale bridge condition monitoring using InSAR displacements and environmental data

This article introduces a novel methodology for detecting and classifying anomalies in multiple bridges within a geographical region using satellite-based interferometric synthetic aperture radar displacements and environmental measures. The approach uses subspace alignment to harmonize bridge features, enabling the detection of anomalies based on deviations in one bridge compared to the rest of the population. Simulated and real case studies involving steel railway bridges spanning the Po River in Italy demonstrate the effectiveness of the proposed approach, showcasing its potential for large-scale applications. Moreover, the study explores the transferability of knowledge from simulated data to real-world monitoring scenarios, yielding promising results in classifying real instances using synthetic labels. The proposed approach presents practical benefits for bridge monitoring agencies by providing a cost-effective method for enhancing the resilience and safety of transportation infrastructure.

An intelligent rule-based decision-making system for preliminary truck dispatching within open-pit mines

Preliminary truck dispatching involves directing trucks to appropriate destinations before addressing specific optimization objectives such as maximizing ore production or minimizing waiting times. While rule-based systems are commonly used for preliminary dispatching, they lack adaptability to unforeseen scenarios. This study presents an intelligent rule-based system that integrates reinforcement learning to generate labeled data and supervised learning to train a deep neural network on the collected data. A modified Q-learning algorithm, RapidQ, was introduced to expedite the data collection process. The system was implemented in a simulated open-pit mine case study, which incorporated a broader range of dispatching features that are underexplored but essential compared to previous studies. The simulation was designed to handle uncertainties such as weather conditions, blasting needs, truck and shovel failures, and maintenance schedules. When evaluated against a conventional rule-based system, the proposed intelligent dispatching system achieved 10 times fewer incorrect dispatches, 4% fuel savings, a 10% reduction in queuing time, and a 14% increase in ore production. The developed system can be positioned as a potential upper-stage solution in future multi-stage intelligent dispatching systems, complemented by specific dispatching algorithms at the lower stage.

Optimizing System Performance with Adaptive Algorithms for Signal Processing in Wireless Communication Networks

In modern wireless communication networks, the need for high-speed, reliable, and efficient data transmission is paramount. Signal processing algorithms play a critical role in optimizing network performance, especially under dynamic conditions such as varying network loads and environmental interference. This paper presents an in-depth study of adaptive signal processing algorithms designed to improve the performance of wireless communication systems. We examine various techniques, including adaptive filtering, channel estimation, and interference cancellation, and assess their effectiveness in optimizing signal quality and network throughput. Through simulation and real-world case studies, the paper demonstrates how these algorithms can be applied to mitigate signal distortion and reduce data transmission errors in wireless networks. The results show that adaptive algorithms significantly enhance the overall system performance, particularly in environments with fluctuating signal conditions. The paper concludes with recommendations for further development in adaptive algorithms for future wireless communication standards, particularly 5G and beyond.

Analytical and numerical study on radius of investigation in composite reservoirs

Radius of investigation (ROI) is of great significance in well testing and reserve estimation. The studies on ROI over the past decades were mainly focusing on homogeneous reservoirs. In heterogeneous formations, such as composite reservoirs and fractured reservoirs, the problem remains unsolved. Few ROI evaluation methods for composite reservoirs have been validated and commonly accepted. This paper conducts extensive analytical and numerical analysis on the pressure propagation of composite reservoirs, analyzing the limitations and misunderstandings of previous methods on the evaluation of ROI. The pressure solution of a two-zone composite reservoir is presented in the Laplace domain and is used to analyze the impact of heterogeneity parameters on pressure transient propagation and the evaluation of radius of investigation. A new ROI formula for composite reservoirs is proposed, which is in great agreement with the analytical solution. The study reveals that the inner zone of a composite reservoir cannot be treated as homogeneous when the pressure front approaches the interface of the two zones with high heterogeneity. A large mobility ratio will slow pressure propagation in composite reservoirs, while a low storativity ratio will increase the value of ROI. Reservoir simulation and real case studies are used to demonstrate the findings of the analytical study. The new study sheds insights into the pressure propagation and eases the calculation of ROI in composite reservoirs.

Fair Collaborative Learning (FairCL): A Method to Improve Fairness amid Personalization

Model personalization has attracted widespread attention in recent years. In an ideal situation, if individuals’ data are sufficient, model personalization can be realized by building models separately for different individuals using their own data. But, in reality, individuals often have data sets of varying sizes and qualities. To overcome this disparity, collaborative learning has emerged as a generic strategy for model personalization, but there is no mechanism to ensure fairness in this framework. In this paper, we develop fair collaborative learning (FairCL) that could potentially integrate a variety of fairness concepts. We further focus on two specific fairness metrics, the bounded individual loss and individual fairness, and develop a self-adaptive algorithm for FairCL and conduct both simulated and real-world case studies. Our study reveals that model fairness and accuracy could be improved simultaneously in the context of model personalization. Funding: This work was supported by the Breakthrough T1D Award [Grant 2-SRA-2022-1259-S-B]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijds.2024.0029 .

Quantum Networks and Cyber Security. A Simulation Case Study for Point-to-Point QKD Links

Quantum Networks and Cyber Security. A Simulation Case Study for Point-to-Point QKD Links

Enhancing Production and Logistics Efficiency Through Dynamic Simulation: A Case Study in Transport System Optimization

Abstract Dynamic computer simulation verifies before implementation how the production and logistics process system will work after implementation It is equally important in process automation when bottlenecks in processes are found and need to be optimised. Simulation can be used to verify the outcome of changes in production, logistics, warehouse or management processes before they are implemented in the real system. In the digital environment, experiments are conducted with internally deployed systems or processes, for example, the impact of process sequencing, orders, random occurrence of equipment failures, their retyping, the impact of logistics on production performance, and others. The customer thus avoids potential high financial and capacity losses resulting from additional modifications to the real system and gains certainty in decision-making in the planning phase. The aim of this paper is to provide a detailed analysis of the transport system in industry and to develop a simulation case study of a transport system. The simulation will be used to verify the proposed innovations in the case study.

Real‐Time Energy Management System for a Hybrid Renewable Microgrid System

ABSTRACTThis paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm.

Advanced transient switching and coordinated power control strategies for flexible interconnection of multiple microgrids

Multiple microgrid (MG) distribution systems are facing challenges owing to variations in the operational statuses of the individual MGs, which experience voltage and current fluctuations during transient interconnections. The impedances of the interconnecting lines further exacerbate the unevenness of power distribution among the MGs, hence threatening the operational stability of the system. To achieve flexible and seamless interconnections between multiple MGs, we fully analyzed the interconnected structures and operation modes of the MGs; then, we designed a transient switching control method based on investigation of the transient interconnection processes to ensure smooth transition of the MGs. Additionally, to balance the power distribution among the interconnected MGs, a voltage–current-based coordinated power control strategy was synthesized using advanced synchronized fixed-frequency technology. Simulation case studies were conducted, and the results indicate that the proposed coordinated power control scheme effectively facilitated instantaneous interconnections between the isolated regions, thereby avoiding voltage disturbances and current surges. Furthermore, it efficiently equalized and distributed the output power from the distributed energy sources, thereby enhancing the operational flexibilities and stabilities of the MGs and distribution system.

Case study of a world hour record simulation in an elite cyclist: Insight into task failure.

The 'cycling hour-record' is one of the most prestigious events in cycling. However, little detailed analysis of such attempts is available. In preparation for a successful cycling hour-record attempt, an elite cyclist performed a full-hour simulation to provide insights into performance, physiological, aerodynamic and biomechanical limitations that could be identified in the preparation for a subsequent official attempt. Performance (speed, lap time, power and cadence), physiological (heart rate and estimated body temperature), aerodynamic (CDA, helmet angle, rotation and rock) and biomechanical (helmet, thigh and foot position changes) measurements were made throughout the attempt, in which an even-pacing strategy was employed where the point of task failure was defined as the lap which the rider could no longer perform at the targeted lap split (16.6s) or quicker. The cyclist did not achieve the target distance (54,000m) during the simulation. The final distance achieved for the hour was 53,250m. Task failure occurred at 38min and 33s (lap 139/34,750m) into the simulation. Notably, there was a decrease in power output, accompanied with an increase in the estimated body temperature, changes in pedalling kinematics and an increase in aerodynamic drag. The reduction in performance (leading to task failure) during a cycling hour record simulation is underpinned by a decrease in power output as well as an increase in aerodynamic drag due to biomechanical changes in the cycling technique.