Search Results (643)

The sortie generation rate (SGR), a key metric of carrier-based aircraft operations, is severely degraded by multi-source coupled faults across the human–equipment–environment triad. Existing models oversimplify these dynamics by employing static failure probabilities and treating contributing factors in isolation, thereby underestimating systemic risk. To address this, we propose a mechanism-driven, hybrid simulation framework that dynamically captures fault coupling and cascading effects within the phased-mission system (PMS) of flight deck operations. First, 22 basic fault events are identified via fuzzy fault tree analysis (FFTA) and translated into a Bayesian network (BN) to establish a probabilistic baseline. A multi-source coupled fault model is then constructed, integrating human reliability, time-varying equipment degradation, and fault stress propagation to describe spatiotemporal coupling. A protocol is designed to robustly simulate heterogeneous fault dynamics within a discrete-continuous hybrid engine. Simulation experiments demonstrate that: (1) the baseline replicates real-world exercise data, validating framework credibility; (2) the model reveals a nonlinear SGR degradation with a sharp decline beyond a critical maintenance-pressure threshold, a behavior missed by static models; and (3) a comprehensive maintenance strategy improves long-term SGR by 73.13% over a reactive baseline. This framework provides a scalable testbed for evaluating operational resilience and informing maintenance strategies for next-generation aircraft carriers. Full article

Container ports are pressured for efficiency while reducing greenhouse gas (GHG) emissions under decarbonization rules. Simulation-based operational planning frameworks are increasingly being explored for container terminal decarbonization and coordination analysis. This study fills the gap by developing a trace-driven offline digital twin (DT) framework integrated with a discrete event simulation model decision support for tactical planning and emissions assessment at Tanjung Priok International Container Port. Historical terminal operating system data were used to reproduce real operational conditions and evaluate alternative operational strategies without disrupting live terminal operations. Using deterministic simulations, the effects of coordination strategies, such as yard allocation, equipment deployment, and truck–vessel synchronization, on crane productivity, turnaround time, equipment utilization, and emissions were analyzed. The strategies impacted productivity, truck turnaround time, equipment utilization, and lifecycle GHG emissions. Monte Carlo simulations, the uncertainty and variability of environmental outcomes under different operational conditions were analyzed. The results show that coordination-based strategies outperform simple expansion in reducing congestion and emissions. Truck–vessel synchronization strategies and workload redistribution across yard space and time provide substantial operational and environmental benefits. The proposed framework should therefore be interpreted as a trace-driven offline DT discrete event simulation environment for tactical scenario evaluation rather than a fully synchronized real-time digital twin. Full article

Slip ring–brush assemblies are widely used in satellite mechanisms to transmit power and signals across rotating interfaces. Under authentic space environments—vacuum, radiation-dominated thermal exchange, and long-duration operation—the coupled effects of mechanical contact dynamics, electrical conduction, intermittent separation, and arcing can accelerate wear and degrade reliability. This paper presents a surface-resolved multiphysics model for multi-track slip rings with staggered brushes. The ring surface is discretized on a circumferential–axial grid and endowed with correlated 3D roughness, enabling interference-based asperity contact. Brush normal dynamics (mass–spring–damper) convert runout and micro-vibration into normal-force ripple and separation events. Electrical conduction is modeled by a parallel admittance network combining pressure-dependent micro-contact conduction and an event-based arc channel activated by separation, opening velocity, and current density with stochastic ignition. A 2D thermal model with ADI integration accounts for Joule/friction heating, radiative cooling, and optional hub conduction. Wear evolves via an Archard-type mechanical term and an arc-energy-driven erosive term. A FAST–MACRO multiscale scheme (20 s FAST, 100 h MACRO with periodic recalibration) enables tractable long-horizon wear prediction while preserving arc statistics. Baseline simulations for a 28 V bus demonstrate rare but nonzero arc activity and predict spatially non-uniform wear at the micrometer scale after 100 h. Full article

Truck-to-excavator assignment is a time-critical control problem in open-pit earthmoving systems (mines, quarries, and large cut-and-fill construction sites) where stochastic travel and service times, changing queues, and equipment outages continually alter the best dispatch decision. A deep reinforcement learning (DRL) dispatch policy is developed and trained using a discrete-event simulation (DES) digital twin of the Sungun copper mine haulage system. The dispatch task is formulated as a Markov decision process using state features that represent fleet locations, excavator and dump queues, and short-term congestion conditions. The resulting deep artificial neural network (DANN) policy is tuned via systematic hyperparameter optimisation and evaluated against a priority-based rule-of-thumb dispatch baseline under long-horizon operating tracks. Results show that the final trained policy improves the average production rate per truck cycle by approximately 17% while reducing avoidable waiting and maintaining stable performance over extended operation, with inference fast enough for real-time dispatch use. Model fidelity is supported by close agreement between simulated and observed daily completed-cycle counts. Robustness is assessed through controlled truck load-capacity perturbations, and scalability is examined through fleet-size sensitivity, which reveals diminishing returns as additional trucks are added under a fixed excavation–haulage configuration. Practical deployment considerations and implications for construction earthmoving logistics are discussed. Full article

This paper investigates the problem of non-fragile dissipative filtering for discrete-time nonlinear networked systems with dynamic quantization, a dynamic event-triggered mechanism and stochastic cyber attacks. The nonlinear networked system under investigation is described by an uncertain Takagi–Sugeno (T-S) fuzzy model. In this work, a novel fuzzy-dependent dynamic event-triggered communication scheme and the dynamic quantization strategy, integrated with an online adjustment rule, are introduced to reduce the frequency and volume of data transmission, thus realizing more rational utilization of the limited communication resources. In addition, the stochastic cyber attacks are characterized by a random variable obeying the Bernoulli distribution. The core focus of this paper is to design a non-fragile filter such that the resulting filtering error system is stochastically stable and meets the prescribed dissipative filtering performance. Based on the matrix inequality decoupling technique, the design conditions of the desired filter are derived and presented in the form of linear matrix inequalities (LMIs). Finally, the effectiveness and superiority of the proposed filter design approach is verified via two simulation examples. Full article

Airport systems rely on tightly connected digital and physical components, so cyber disruptions can affect both service performance and passenger movement. Existing airport simulation studies often focus on either queue-based passenger processing or pedestrian movement but rarely combine both in a framework suited for cyber-resilience analysis. This paper presents a hybrid simulation framework that integrates discrete-event simulation (DES), JuPedSim-based microscopic pedestrian modeling, and structured large language model (LLM) decision support to examine how cyber disruptions propagate through passenger-facing airport operations. The DES layer models service processes such as check-in, information desks, and security screening, while the pedestrian layer models movement, congestion, route choice, and spatial occupancy. Under degraded display or guidance conditions, the LLM generates structured passenger-level post-security decisions, such as going directly to the gate, checking a display, asking staff, waiting, visiting optional activity areas, or first moving to a wrong intermediate area. The framework is evaluated through a 500-passenger terminal case study with one baseline case and four disruption cases. Results show that check-in and security degradation produce the largest throughput loss, queue growth, and completion-time increase, while guidance degradation mainly affects post-security behavior. Spatial heatmaps further show where bottlenecks emerge and how congestion shifts across the terminal. Additional Rotterdam checkpoint validation, Palma benchmark analysis, and LLM ablation results support the framework’s ability to reproduce plausible queue, timing, throughput, and behavior-sensitive disruption patterns. The study provides a practical methodology for exploratory airport cyber-resilience assessment under coupled service, movement, and degraded-guidance conditions. Full article

Time-varying delay and limited communication resources pose two fundamental challenges to the stability and efficiency of discrete-time cascade control systems (CCS). To address these issues, this paper presents, for the first time, the co-design of an event-triggered PI–P controller for discrete-time CCS with time-varying delay. A discrete-time state-space model with time-varying state delay is first established to accurately characterize the cascade dynamics. An event-triggered mechanism (ETM) is then introduced to effectively reduce redundant data transmissions while maintaining desired control performance. Based on Lyapunov stability theory, sufficient conditions are derived to enable the joint synthesis of the primary Proportional (P) controller, the secondary Proportional–Integral (PI) controller, and the event-triggering thresholds in an integrated manner. Simulation results confirm that the proposed method delivers improved transient performance and steady-state accuracy while reducing control update frequency, thereby providing a superior balance between control performance and communication efficiency compared with conventional event-triggered P–P cascade control strategies. Overall, the proposed co-design framework offers an effective and systematic solution for both enhancing stability and reducing unnecessary control updates in discrete-time CCS subject to time-varying delay. Full article

Silicon–germanium (Si/SiGe) quantum dots represent a preeminent architecture for scalable quantum computing; however, their performance remains fundamentally constrained by environmental decoherence. This work presents a comparative simulation study of a two-qubit system in Si/SiGe, evaluating the fidelity of various noise modeling frameworks under realistic conditions, including $1/f$ charge noise and phonon-mediated relaxation. We benchmark the Lindblad Master Equation against the Bloch–Redfield Master Equation, the Semiclassical Stochastic Hamiltonian method and the Monte Carlo Wavefunction (Quantum Jumps). Our analysis reveals that while semiclassical models effectively capture pure dephasing ($T_2^{*}$) dynamics, they fail to account for energy relaxation ($T_1$) at cryogenic temperatures, erroneously driving the system toward a high-entropy maximally mixed state. We propose the Quantum Trajectories method to resolve this discrepancy by incorporating discrete dissipation events, providing a thermodynamically consistent semi-classical framework. To demonstrate the scalability of our approach, we extend the simulation to a 4-qubit register, showing that the Quantum Trajectories method remains numerically robust and thermodynamically consistent as the Hilbert space dimension increases. Furthermore, we perform a magnetic field optimization analysis, identifying an operational “sweet spot” within the 0.1–0.5 T range that optimally balances the trade-offs between relaxation and dephasing. Full article

Flash-flood and floodplain inundation simulations are highly sensitive to the spatiotemporal variability of convective rainfall, particularly during the initial runoff generation stage. However, coarse-resolution numerical weather prediction (NWP) forcing tends to smooth localized rainfall extremes, limiting its ability to accurately represent hydrological responses in low-relief floodplains. In this study, we couple a diffusion-enhanced radar nowcasting model, Diff_ConvLSTM, with a spatial resolution of 1 km and a temporal resolution of 6 min, to assess the hydrological value of high-resolution rainfall forcing over the middle Yangtze River floodplain. We introduce a monotone piecewise cubic Hermite interpolation scheme to ensure a stable transition from discrete high-frequency rainfall inputs to continuous hydrodynamic integration. Evaluation using a radar dataset from 2023 to 2024 shows that Diff_ConvLSTM better preserves intense convective echoes and rainband structures compared to the baseline ConvLSTM, increasing the Probability of Detection at the 40 dBZ threshold by 65.8%. A forcing-replacement experiment for the flood event on 30 June 2023 demonstrates that AI-based nowcasting rainfall forcing reduces peak-discharge underestimation, improves volumetric consistency, and produces inundation patterns that are closer to the observation-driven reference than those generated by low-resolution forecast forcing, although positive biases in inundation area and water depth persist. An additional event in 2024 confirms that the improvements are primarily reflected in discharge magnitude and flood volume representation, while enhancements in peak timing remain limited. Overall, the results illustrate both the added value and the remaining limitations of AI-enhanced nowcasting for hydrologically informed flood forecasting. Full article

This study investigates the impact of selected automation scenarios on occupational safety, ergonomics, and operational performance in a plastic crate production workstation. The research focuses on a specific case from the discrete manufacturing sector and aims to develop an integrated analytical framework combining ergonomic assessment with process simulation for the evaluation of organizational and technological improvements in manual handling operations. This study applies a simulation-based production model developed in the DBR77 discrete-event simulation environment to analyze alternative workstation configurations. The assessment framework integrates Ishikawa analysis for root-cause identification and the RULA and REBA methods for ergonomic risk evaluation. The investigated workstation was characterized by repetitive manual handling activities, awkward working postures, and increased physical workload associated with palletizing and transport operations. Several organizational and technological variants were analyzed, including additional operator support, robot-assisted palletizing, conveyor integration, and automated guided vehicle (AGV) transport. The simulation results indicated that the AGV-supported configuration achieved the shortest cycle time (1270 s per batch of 30 units), whereas the robot-assisted variant resulted in the longest cycle time (1520 s). Ergonomic assessment showed a reduction in RULA scores from 6–7 to 3–4 and REBA scores from 8–10 to 4–5 in the automated scenarios. The contribution of this study lies in the integration of ergonomic risk assessment and discrete-event simulation within a unified evaluation framework for workstation redesign in discrete manufacturing environments. The findings demonstrate how simulation-supported analysis can support decision-making regarding the balance between manual labor and automation under specific operational conditions. Due to the single-case-study design, the results should be interpreted as context-specific and exploratory rather than directly generalizable to all manufacturing systems. Full article

Heavy-haul railways require efficient rail replacement because extreme axle loads and high-density transport accelerate rail wear. Traditional manual-led processes are limited by fragmented operations, high labor demand, and complex equipment scheduling, typically completing about 1 km of rail replacement within a 4 h maintenance window and requiring approximately 340 workers. This study is positioned as construction-process modeling, workflow organization, and simulation-supported feasibility analysis for an integrated rail-changing workflow, rather than the development or field validation of a fully mature rail-changing machine. The proposed workflow coordinates rail unloading, on-board welding, fastener disassembly, rail cutting, exchange-recovery, fastening, closure welding, and final inspection through a highly parallel construction organization. A process-level train-set configuration, including a tractor, a long-rail comprehensive transport vehicle, an exchange-recovery integrated transport vehicle, and a mobile welding vehicle, is used as an engineering carrier to support the closed-loop workflow of unloading, welding, exchange, and recovery. Based on engineering time-study analysis, field experience, expert consultation, and discrete-event simulation, the results indicate that the proposed workflow has the potential to complete a simulated 2 km rail-changing task within a single 4 h maintenance window with an estimated labor demand of 80–95 personnel under the specified assumptions. The study provides conceptual and simulation-supported feasibility evidence for construction-process organization, rather than field-validated machine performance, and offers a technical reference for improving the mechanization and coordination of heavy-haul railway maintenance. Full article

In the construction of conventional concrete high arch dams in high-altitude regions with large temperature variations, the prolonged and cold winters often force the suspension of concrete pouring, severely constraining the overall schedule. To address this limitation, this paper breaks away from the conventional winter-shutdown scheme by proposing a new technique: continuous construction under low-temperature conditions. It can adapt to large temperature variations, and this study develops a corresponding construction schedule simulation model for quantitative evaluation and scheme optimization. First, the influence of large diurnal temperature variations on high-altitude concrete pouring was analyzed. Based on this, a dynamic pouring technique for sub-blocks is proposed—thin-layer pouring during positive temperatures and insulation curing during negative temperatures—with the aim of transforming discrete climatic windows into a continuous construction period. Second, to accurately simulate this complex spatial partitioning and temporal scheduling process, a customized schedule simulation model based on discrete-event simulation (DES) theory was developed. The model incorporated meteorological recognition at low temperatures, dynamic dam-block partitioning, and sub-block pouring scheduling. Finally, a high arch dam on a plateau in Southwest China was used as an engineering case to compare two construction schemes: the low-temperature shutdown scheme and the continuous construction scheme. After validating the simulation model under parameter assumptions such as ideal resource availability and stable annual climate patterns, the results showed that the continuous construction scheme achieves a monthly average pouring volume of 33,721 m³ during the period with large diurnal temperature variations, which accounts for 42.48% of the average monthly pouring volume during the normal construction period. Compared to the low-temperature shutdown scheme, the coefficient of variation of the monthly pouring intensity decreases by about 40%, and the total construction period is shortened by approximately ten months. This demonstrates the potential for schedule optimization for continuous winter construction in simulation. Full article

Modeling over-dispersed count data is a common challenge in applied statistics, especially in engineering applications where repeated events, system faults, and clustered observations often produce variability beyond that allowed by the classical Poisson model. In this paper, we introduce and study the Poisson–QGamma distribution, a new compound discrete model obtained by mixing the Poisson distribution with the QGamma distribution. The proposed distribution is analytically tractable and flexible enough to capture over-dispersion, skewness, and excess kurtosis, which are frequently observed in real count data. Several statistical properties of the distribution are derived, including the probability mass function, cumulative distribution function, survival and hazard rate functions, moments, dispersion index, skewness, kurtosis, entropy, and generating functions. Parameter estimation is considered using maximum likelihood, method of moments, least squares, and weighted least squares methods. The finite-sample behavior of these estimators is examined through Monte Carlo simulation. A regression model based on the Poisson–QGamma distribution is also developed for count responses with covariates. The proposed model is compared with classical and competing count models using simulation and real-data applications. Three engineering-related datasets, involving power grid failure counts, environmental sensor event counts, and packet loss counts in communication networks, are analyzed to illustrate the practical value of the model. The results show that the Poisson–QGamma model provides a better fit than several standard alternatives, including the Poisson, negative binomial, Poisson–Lindley, generalized Poisson, and COM–Poisson models, particularly in the presence of over-dispersion and heavy-tailed behavior. Overall, the proposed distribution offers a parsimonious and effective tool for modeling over-dispersed count data, while also contributing to the broader class of compound discrete distributions. Full article

Sustainable urban logistics requires infrastructure that can support routine low-carbon freight delivery while maintaining emergency supply capacity under disruptions. However, existing underground logistics system studies mainly focus on routine freight efficiency and network feasibility, whereas emergency logistics research is largely based on surface transport systems. Limited attention has been paid to the integrated design and operational validation of dual-use underground logistics networks under uncertain routine and emergency demand. To address this gap, this study proposes a dual-use underground logistics system (DULS) framework that combines robust layout optimization with dynamic simulation. A multi-echelon network consisting of supply centers, primary nodes, secondary nodes, and demand points is constructed. Candidate primary nodes are screened using an entropy-weighted TOPSIS method, and a Wasserstein-based distributionally robust optimization model is formulated to jointly determine node location, resource allocation, and freight paths under demand uncertainty. A hybrid heuristic is developed to solve the model, and an AnyLogic-based discrete-event simulation model is used to evaluate operational performance under different demand-generation patterns and train operation strategies. In the Nanjing case, the optimized DULS includes 19 primary nodes and 72 secondary nodes, achieves an emergency-demand fulfillment rate of 84.84%, and keeps the average end-to-end emergency supply time within 4 h. Cross-station operation performs better than the all-stop mode in both transport time and deprivation cost. An ex-post operational emission comparison further indicates that the DULS can reduce road-based freight emissions by 60.20% under routine operations. The proposed framework provides methodological support for planning sustainable dual-use underground logistics infrastructure serving both routine freight delivery and emergency supply. Full article

Tomographic volumetric additive manufacturing (VAM) is an innovative 3D printing technology that polymerizes an entire volume of photopolymer resin simultaneously. VAM enables an increased printing speed and higher output compared with traditional stereolithography, layer-by-layer printing. We explore the operational implications of adopting VAM in an intelligent manufacturing context by considering process planning and production control issues exacerbated by the time bottlenecks introduced in downstream post-processing stages. Discrete Event Simulation (DES) was used to model production flow for two conceptual scenarios: a small-batch low-mix production environment and a high-mix variable-batch production environment. We simulated production, analyzed bottlenecks and tested intervention strategies that may be implemented: (1) increasing the availability of post-processing equipment, (2) modifying the number of available printers and (3) implementing improved workforce scheduling to reassign skilled operators during downtime of certain machines to reduce waiting time. VAM can speed up the creation of the primary part, but post-processing steps such as curing, washing and finishing the produced part might nullify those savings. Through the intervention methods we studied, the overall system utilization rate can be increased. VAM can achieve higher throughput rates in intelligent manufacturing settings only when it is incorporated into intelligent planning systems with high-speed post-processing. We provide some operational considerations in scaling up the VAM manufacturing capability, specifically focusing on planning challenges and gaps in adoption within manufacturing contexts. In this context, we find that coupling data-driven simulation methods with process planning algorithms may further improve workflow in smart manufacturing environments. Full article