Operations Research, Systems Engineering and Industrial Engineering Commons^™

Large Language Model-Assisted Control Framework For Mixed Remote Sensing Satellite Constellations, Guohua Wu, Ziyan You, Lei Li, Yi Gu, Shuanglin Li

Journal of System Simulation

Abstract: A LLM-assisted control framework for mixed remote sensing satellite constellations is proposed to address the problem that the control and scheduling of mixed remote sensing satellite constellations cannot process users' raw natural language observation requirements and realize full-process automation from requirement input to scheduling schemes. LLMs are utilized to enhance the construction of domain knowledge graphs, requirement understanding and task characterization, and realize intelligent generation of scheduling schemes and optimal solution of scheduling models. A scheduling scheme generation method based on the "planning and execution" dual-Agent architecture is designed. Combined with the constellation scheduling toolset, the capability boundary of …

On-Orbit Refueling Mission Planning Of Satellites Based On Improved Nsga-Ii, Guozheng Li, Rui Wang, Yi Lu

Journal of System Simulation

Abstract: An improved NSGA-II solution framework based on multi-tree encoding is proposed to address the problems of high modeling complexity under large-scale scenarios and the tendency of traditional algorithms to fall into local optima. Based on multi-tree modeling, the complex mixed-integer programming problem is transformed into a multi-tree configuration optimization problem, thus reducing the constraint handling difficulty and improving the search efficiency of the solution space. Multi-tree crossover and mutation operators as well as the calculation method for objective functions are designed. A large neighborhood search (LNS) strategy is introduced, and the global exploration and local exploitation capabilities of the …

High-Fidelity Dynamic Digital Twin Modeling Method For Permanent Magnet Synchronous Motor, He Lai, Jinquan Yang, Cong Peng, Bei Li

Journal of System Simulation

Abstract: Conventional mechanism models often suffer from model mismatch due to the absence of nonlinear mechanical damping representation and the dynamic drift of electromagnetic parameters. To address this issue, a high-fidelity dynamic digital twin modeling method for permanent magnet synchronous motors is proposed. In the static mechanism modeling dimension, a friction energy decoupling strategy is proposed, which fundamentally overcomes the inherent conflict between steady-state accuracy and transient response from a physical logic perspective. For dynamic model updating, a distributed cascaded sensing architecture based on the multi-innovation recursive least squares and extended Kalman filter (MIRLS-MIEKF) algorithms is proposed. This architecture enables …

Scheduling Optimization For Cascaded Hybrid Flow Shop Integrating Llm And Rl, Fuqing Zhao, Bin Wu, Tianpeng Xu, Ningning Zhu, Houbin Song, Yukai Yao

Journal of System Simulation

Abstract: For the cascaded hybrid flow shop scheduling problem with AGV transportation, a multiobjective optimization model is built to minimize the makespan and total energy consumption. A hybrid algorithm framework integrating the LLM, covariance matrix adaptation evolution strategy, and RL strategy selection mechanism is proposed. LLMs are employed to generate heuristic priority rules and construct high-quality initial solutions. Covariance matrix adaption evolution strategy is adopted to optimize the search in continuous priority space, and an enhanced scalar fitness function integrating hypervolume gain and solution diversity is introduced. An RL strategy selection module based on the upper level information boundary is …

Q-Learning Enhanced Memetic Algorithm For Robust Scheduling Of Space Launch Missions, Rui Lin, Xiaolu Liu, Yonghao Du, Mingyang Geng, Zhenshun Shi

Journal of System Simulation

Abstract: A robust optimization mathematical model and an enhanced memetic algorithm (MA) for space launch mission scheduling are proposed to address the practical challenges in space launch mission scheduling under the influence of multiple uncertainties such as equipment failure, process rework due to quality issues, and technological changes. A robust optimization scheduling model for space launch missions is built by integrating the makespan under the worst-case scenario, the average maximum makespan, and the average deviation of process start times. A Q-learning enhanced memetic algorithm (QMA) is proposed, which can dynamically select parent solutions based on the population state. Simulation experiments …

A Macroergonomic Analysis Of Autonomous Robotic Construction For Affordable Housing, Ricardo Leon Jimenez

Sustainability Conference

As communities seek to increase housing supply, the construction sector continues to grapple with rising costs, skilled labor shortages, and growing pressure to improve environmental sustainability. Meeting these demands will require new approaches that increase productivity while lowering environmental impact throughout the building lifecycle. Recent advances in autonomous systems developed for space exploration offer alternative ways to design, assemble, and maintain residential buildings. This study examines the potential application of NASA's Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) technology from space construction to terrestrial affordable housing construction.

While most construction automation research focuses on technical performance, this study considers …

On The Issue Of Developing Diagnostic Systems In Case Of Incompleteness Of Data, Amanulla Azizovich Kadirov, Shohijahon Ulugbek Ugli Akhmetov

Technical science and innovation

This paper addresses the problem of developing diagnostic systems for industrial equipment when initial failure data is incomplete or unavailable. An approach is proposed for generating synthetic data based on a pseudo-failure generator that implements stochastic modeling of degradation processes. A formalized algorithm for generating time-to-failure samples has been developed, including stages of failure physics analysis, statistical model selection, distribution parameterization, and simulation of operating conditions. The Weibull distribution is used as the base model, allowing for the consideration of various stages of the equipment’s life cycle. Additionally, factors that bring the synthetic data closer to real operating conditions are …

Mathematical Modelling Of Nitrification And Denitrification Processes Based On Neuro-Fuzzy Bioreactor Models, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon Ugli Mannobjonov

Technical science and innovation

This paper presents the development and investigation of hybrid neural network and fuzzy models for the mathematical modelling of nitrification and denitrification processes in a biological wastewater treatment bioreactor. A comprehensive approach is proposed, integrating a mechanistic model of the ASM1/ASM2d type with neural networks (LSTM and Gaussian Process Regression), as well as a fuzzy control system based on an extended set of expert rules. A digital twin of the bioreactor was developed to allow for the prediction of the dynamic behavior of key parameters such as NH₄⁺, NO₃⁻, dissolved oxygen, etc. within a prediction range of 1 to 12 …

Integrating Gis With Interim Payment Valuation In Road Construction Projects: A Conceptual Framework, Abdulrahman I. Iro, Juma M. Matindana, Julian Ijumulana

Tanzania Journal of Engineering and Technology (TJET)

Abstract

Interim Payment Valuation is a critical process in road construction contract administration, yet conventional valuation practices remain heavily dependent on manual measurements, fragmented documentation, spreadsheets, and professional judgement. These limitations can affect measurement accuracy, transparency, traceability, and the timeliness of payment certification. Although Geographic Information Systems have increasingly been applied to construction planning, quantity measurement, progress monitoring, infrastructure management, and decision support, their integration with contractual and financial processes for interim payment valuation remains insufficiently explored. This study therefore develops a conceptual framework for integrating GIS with Interim Payment Valuation in road construction projects. A PRISMA-guided structured literature review …

Artificial Neural Network-Based Modelling And Prediction Of Key Performance Indicators In Road Construction Projects, Hussein Mativila, John M. Kafuku, Beatus A. T. Kundi

Tanzania Journal of Engineering and Technology (TJET)

Road construction projects in developing countries including Tanzania experience several challenges including cost overruns, schedule delays and poor quality. The causes of these challenges include complex interdependencies project’s uncertainty factors. This makes traditional methods for predicting Key Performance Indicators (KPIs) less effective. This study has developed a robust Artificial Neural Network (ANN) model for accurate modelling and predicting KPIs for road construction projects in Tanzania. The analysis was based on data obtained from 281 projects implemented by TANROADS in 11 regions from 2015 to 2025. Fourteen uncertainty factors were measured on a five-point Likert scale and screened using Principal Component …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Enhancement In Surface Finish Of Additively Manufactured 17-4ph Stainless Steel Using A Hybrid Chemical Machining–Magnetic Abrasive Finishing Approach, Harish Kumar, Gurpreet Singh, Anil K. Srivastava, Majid Hosseini

Manufacturing & Industrial Engineering Faculty Publications

Additive Manufacturing (AM) is a cutting-edge technique utilized for the precise manufacturing of complex components made from a variety of material compositions. Although AM, generally, produces a better surface finish than traditional subtractive methods, additional post-processing is required to attain the desired surface characteristics. One commonly used AM material is 17-4PH stainless steel, which is majorly employed in modern industries like aerospace, marine, petroleum, chemical, medical, and firearms due to its superior physical and chemical properties compared to other stainless steels. Surface finish of additively fabricated 17-4PH stainless steel can be further improved using various finishing techniques. This study presents …

Thermal Displacement Quantification And Dynamic Disparity Compensation For Automotive Stereo Cameras, Bisheng Wang, Xiyuan Hu, Ran Meng, An Jiang

Journal of System Simulation

Abstract: This paper presents a full-field thermal displacement inversion and dynamic disparity compensation method to address the disparity drift issue in mass-produced automotive-grade stereo cameras induced by thermal expansion and rigid-body displacement of image sensors under wide-temperature-range operating conditions. The proposed method leverages a high-precision static speckle pattern and digital image correlation (DIC) technique to achieve quantitative characterization of the nonuniform horizontal thermal deformation fields of the left and right image sensors. On this basis, a dynamic disparity compensation model is established, integrating a temperature-dependent quadratic polynomial translation term and a radial thermal expansion correction term. Experiments were conducted by …

Vehicle Cross-Modal Trajectory Prediction Model Based On Driving Style Hypergraphs, Guozhu Cheng, Changyuan Wang, Wenzhi Wang, Yifan Zhang, Tianjun Feng

Journal of System Simulation

Abstract: To address the shortcomings of existing motorway vehicle trajectory prediction methods which overlook differences in driving styles and the future interactions between trajectories of different modes. This paper proposes a cross-modal trajectory prediction model based on a dual-hypergraph Transformer for driving styles. The method first quantifies driving styles by extracting vehicle kinematic features and calculates a similarity matrix between vehicles; it then constructs a dual-hypergraph Transformer, using a homogeneous hypergraph to model the group interactions of vehicles with similar driving styles, and a heterogeneous hypergraph to capture the interaction relationships between spatially neighbouring but stylistically dissimilar vehicles; Finally, a …

Long-Tail Object Detection Method In Transportation Scene Images Based On Improved Rt-Detr, Mingjun Deng, Peihua Nie, Junhua Guo, Ningbo Gao

Journal of System Simulation

Abstract: Detecting rare objects in transportation environments is crucial for the safety and reliability of autonomous driving systems. To address the challenges of limited detection capability on rare objects that appear only a few times during training, we propose an improved long-tailed object detection method for traffic scene images named TF-DETR (tail free DETR) based on RT-DETR model. First, the lightweight CSP-EDA (CSP-efficient depthwise aggregation) was constructed to reconstruct the backbone network. Its core unit, the efficient depthwise aggregation, integrated the pooled-transpose attention and convolutional gated linear unit to enhance feature representations for tail-class samples. Second, the spatially-adaptive dynamic cognitive …

Vehicle Sar Image Simulation: Progress, Application And Challenge, Shengjie Luo, Zhigang Liu, Xihai Li, Yiting Wang, Xiaoniu Zeng, Xiaofeng Tan, Hongru Li, Tianyou Liu, Yun Zhang

Journal of System Simulation

Abstract: SAR image simulation technology can efficiently generate large-scale annotated data by flexibly configuring system parameters and scene conditions, breaking through the inherent limitations of high cost and long acquisition cycles associated with real SAR images, and effectively mitigating the problem of data scarcity. To elaborate on the overall research status of vehicle SAR image simulation, a classification framework for simulation methods is first established based on technical principles and methodological approaches. Subsequently, a comprehensive review of research progress is provided, offering an in-depth analysis of the theoretical foundations, practical applications, and suitable scenarios of various methods. Finally, based on …

Act Action Chunk Adaptive Optimization Method For Chemistry-Oriented Robots, Tan Li, Congying Wu, Shenghua Li, Nanjiang Chen, Weining Song, Fangfang Gao, Qi Wang, Yanwen Lin, Runmin Yin

Journal of System Simulation

Abstract: Chemical R&D laboratories face persistent challenges such as long R&D cycles and high experimental repetitiveness. With the growing demand for automation and intelligence in laboratory processes, embodied intelligent robots capable of learning expert operational experience and executing tasks stably in complex environments have become a key direction for improving chemical R&D efficiency and reducing human error. Although the hardware and software for embodied intelligence are advancing rapidly, current research focuses primarily on general-purpose operations, with limited attention to the specific constraints of chemistry laboratory settings. Traditional ACT algorithms, which use fixed action chunk sizes, fail to meet the requirements …

High-Dimensional Multi-Objective Optimization Algorithm Based On Large Language Model Assistance, Yichao Ma, Jingxuan Wei

Journal of System Simulation

Abstract: The increase in the dimension of objective functions in optimization problems makes it challenging to balance convergence and diversity in population-based algorithms, which is a key factor leading to performance degradation of multi-objective evolutionary algorithms. To address this issue, a dynamic switching mechanism assisted by a large language model is proposed. By continuously evaluating the changes in hypervolume and convergence metrics of the solution set, the search process is adaptively divided into three phases: convergence-oriented, diversity-oriented, and large language model-suggested phases, achieving precise allocation of computational resources. Furthermore, to mitigate the high computational cost of large language models in …

Modeling, Dynamical Analysis, And Lmplementation Of Switched Memristive Neural Networks, Zhuang Qu, Shaohua Zhang, Cong Wang, Hongli Zhang, Xinkai Li, Ping Ma

Journal of System Simulation

Abstract: To simulate the dynamic functional switching of brain networks, a time-switched memristive Hopfield neural network (TSM-HNN) model is proposed. The model utilizes external time-sequential signals to control memristive synapses, allowing it to dynamically generate multi-scroll chaotic attractors of varying numbers and morphologies without altering the system's structure. This overcomes the limitation of traditional chaotic systems, which can typically only produce a single type of attractor. The model exhibits complex chaotic behavior, high sensitivity to initial conditions, and coexisting attractors. Its physical feasibility has been verified through PSPice circuit simulations. This enhanced complexity and randomness give the model significant potential …

Design And Research Of A Microfocus X-Ray Tube, Haodong Zhao, Shizong Wei, Daolong Yu, Zhixia Liu, Yuezhong Chen, Ji Xu

Journal of System Simulation

Abstract: With the rapid advancement of high-resolution imaging and Micro-nano detection technology, microfocus X-ray sources show great potential for applications in industrial non-destructive testing, biomedical imaging, and material microstructure analysis. Achieving a focal spot size below 10 μm is essential for enhancing spatial resolution. However, traditional X-ray tubes are constrained by heat source dimensions, electron beam divergence, and limited focusing capabilities, which makes it challenging to simultaneously achieve focal spot miniaturization and adequate radiation intensity. In this study, a hot cathode electron gun is modeled using CST particle studio. Simulations are conducted to analyze the influence of the first anode …