Operations Research, Systems Engineering and Industrial Engineering Commons^™

Can Generative Ai Make Farming Decisions? Current Status And Future Pathways: A Case Study In Row Crop Production With Chatgpt, Nipuna Chamara, Yufeng Ge, Joe Luck, Yu Pan, Saleh Taghvaeian, Cory Walters, Christopher Proctor, Daran Rudnick, Daren Redfearn

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in …

A2s Uplink Latency Minimization For Wildfire Monitoring Systems Using Mbse And Stochastic Modeling, Luis Giovanni Wang

Master's Theses

Wildfire response depends on how quickly a detection reaches the people who act on it, and the slowest remaining step is often the link that carries an alert from a remote sensing platform to a satellite. This thesis models the latency of that link, the Air-to-Space uplink, for a wildfire-monitoring UAV that carries a Starlink terminal and sends an ALERT packet to a serving Low Earth Orbit satellite. The uplink is difficult to predict because both the UAV and the satellite move, and because the wildfire environment degrades the channel at the moment the data matters most.

The thesis uses …

Investigation Of Process Parameters To Fabricate Tiwmo Refractory Medium Entropy Alloy Via Laser Powder Bed Fusion, Abdullah Al Masum Jabir, Lindsey A. Salazar, Jianzhi Li

Manufacturing & Industrial Engineering Faculty Publications

This paper presents an experimental study on the fabrication of a TiWMo refractory medium-entropy alloy (RMEA) using laser powder bed fusion (PBF-LB/M, commonly known as selective laser melting) from elemental powders as well as successful alloy formation on titanium substrates. The effects of tungsten particle size and process parameters on successful TiWMo RMEA fabrication have been explored using scanning electron microscopy (SEM), x-ray diffraction, hardness measurement and microstructural analysis. Scanning electron microscope (SEM) analysis revealed that the lowest percentage (0.01%) of unmelted tungsten particles was observed at a laser power of 350 W and scanning speed of 250 mm/s, particularly …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

To Wait Or To Transfer? A Three-Level Optimization Framework For Intermodal Transfer Coordination In First Train Timetabling And Bus Bridging Services Management, Hao Li, Liujiang Kang, Norman Weik, Huijun Sun, Qingying Lai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

This study addresses the integrated optimization of the first train timetabling and bus bridging service design (FTT-BBSD) for morning transfer challenges, two critical but interdependent passenger services in the public transit system. In contrast to most existing studies and conventional approaches, this study explicitly models the influence of passenger path choices and transfer mode selections on FTT-BBSD. Through a novel dual-level network representation that integrates subway and bus systems, we formulate the FTT-BBSD problem as a mixed-integer nonlinear programming model. The model simultaneously determines subway and bus timetables and bridging line deployment to minimize total travel time for all first …

Bridging Images And Language In Radiology: A Comprehensive Prisma Systematic Review Of Transformer Vision-Language Models And Clinical Readiness, Mohammad T. Khasawneh Dr., Sadaf Tabatabaee

Systems Science and Industrial Engineering Student Scholarship

Transformer vision-language models (VLMs) promise end-to-end automation of radiology reporting and related multimodal tasks. However, evidence remains fragmented across datasets, architectures, evaluation practices, and levels of clinical validation, limiting fair comparison and safe translation into practice. Following PRISMA 2020/PRISMA-S, search engines including PubMed, IEEE Xplore, Web of Science, and Google Scholar were systematically searched for peer-reviewed, English-language studies published between 2019 and 2025 that used paired radiology images and free-text reports. Dual reviewers screened records and extracted data using a locked schema covering datasets, modalities, architectures, training objectives, evaluation metrics, and indicators of clinical readiness. Free-text model descriptions were normalized …

Material Costs, Karima Weinman

Masters Theses

This thesis investigates how migration fatality and disappearance data can be reinterpreted through material craft to create a more reflective encounter with information. Working with the Missing Migrants Project's dataset, this project asks how design can communicate dimensions of human loss that conventional data visualization cannot reach.

The work situates contemporary border violence within a longer colonial history, arguing that the logics of surveillance and quantification that structured European imperial expansion persist in the databases that govern mobility in the Mediterranean today.

Terrazzo is a 15th-century Venetian flooring technique built from discarded fragments bound together into a unified surface. This …

Automatic Speed Guidance Method And Simulation Evaluation For Trams At Intersections, Jing Teng, Wencong Tong, Zhongjie Zhang, Xing Yao, Junxian Li

Journal of System Simulation

To address the lack of speed regulation mechanism and the high dispersion of operational time in the current manual driving mode, this paper proposes an automatic speed guidance method for trams at intersections. Considering the speed disturbances caused by potential traffic conflicts at intersections, an initial decision point for safe passage speed at an intersection is established, dividing the operational curve into deterministic segments and disturbance-response segments.To verify the effectiveness of the method, a case study of Songjiang Tram Line 1 is conducted.Driving simulation experiments are performed to obtain manual driving trajectories, and a dynamic trajectory simulation model …

Capacity Market Trading Strategies Of Generators Based On Per-Maddpg Algorithm, Yanbin Li, Zhaolun Pan, Xinyue Ma, Minghao Song, Yujie Hu, Xiaoda Xue

Journal of System Simulation

Considering the issue of how power generators trade off their quantity and price bidding strategies to maximize profits in different capacity market environments, a capacity market bidding equilibrium model is constructed. Recognizing the limitations of traditional solution methods, which rely on the assumption of complete information and have low utilization of historical trading strategy information, a capacity market trading simulation method based on prioritized experience replay multi- agent deep deterministic policy gradient (PER-MADDPG) is proposed. The action space is constructed using quantity bidding strategy and price bidding strategy, and the state space is constructed using historical transaction strategies and winning …

An Automated Generation Method For Combat Simulation Scenarios Based On Large Language Models, Zhiming Dong, Zhongqi Hu, Haoran Dai, Jiancheng Gao

Journal of System Simulation

To address the issue of low efficiency in generating traditional army tactical combat simulation scenarios, an automated generation method based on large language models is proposed. The large language model invokes a semantic segmentation algorithm to parse and restructure the combat scenario, forming semantic modules. Utilizing a multi-agent collaborative framework based on the model contextual protocol, the large language model drives each agent to extract simulation elements from the corresponding semantic modules, constructing a knowledge graph of scenario elements. Using this knowledge graph as a retrieval medium, the method employs a dense retrieval algorithm to achieve precise matching between simulation …

Robust Identification Of Black-Box Nonlinear Ssm Using Expectation-Maximization, Xiaonan Li, Tao Chao, Ping Ma, Ming Yang, Yuxuan Wang

Journal of System Simulation

To address the robust identification problem of nonlinear state space models (SSM) with outliers, missing observations, and unknown state equations, this paper proposes a modeling method based on eigenfunction expansion, Gaussian-process state-space models (GP-SSM), and Student-t distribution. The proposed approach consists of: modeling the state transition function using eigenfunctions and pre-encoding the priors of basis function coefficients via GP-SSM to enhance flexibility; modeling observations as a Student-t distribution with unknown parameters to enhance robustness against outliers; proposing the enhanced particle Gibbs with ancestor sampling (EPGAS) algorithm to adapt to state estimation in scenarios with missing observations; and deriving unknown model …

Multi-Agent Cbs Path Planning Algorithm Based On Minimum Planning Margin First, Longxiao Liang, Jianlin Mao, Niya Wang, Chengyuan Fang, Wenna Zhou

Journal of System Simulation

To address the problems of chain effect and insufficient solving efficiency in the conflict tree (CT) expansion of the traditional conflict-based search (CBS) framework, a minimum-margin-first CBS algorithm based on planning margin was proposed. The calculation of planning margin was introduced into the underlying A* search, and the robots with the minimum margin were prioritized in the high-level conflict resolution, to suppress the chain expansion of the CT while ensuring path optimality.Simulation experiments show that the proposed algorithm significantly reduces the amount of CT node expansion and the number of root node conflicts and effectively improves the solving efficiency, …

Topology Identification Of Complex Dynamical Networks Under Dynamical Saturation Inputs, Haoyu Wang, Yayong Wu, Guoping Jiang, Ying Zheng, Xuanxin Zhou

Journal of System Simulation

In view of the problem that the controller inputs in actual engineering systems are vulnerable to the constraints of dynamical saturation and delayed dynamical saturation, which makes it difficult for the topology identification of complex dynamical networks to adapt to real physical scenarios, a topology identification method based on the drive-response mechanism was proposed. A response network with the same dynamical characteristics and node scale as the original network was constructed, and the dynamical equation of synchronization error between the drive-response networks was established. A controller with dynamical saturation and delayed dynamical saturation and a topology identifier were designed, and …

Robust Two-Stage Mimo-Ofdm Channel Estimation Method Against Sensing Errors, Yi Peng, Jun Wang, Qingqing Yang, Jianming Wang, Hui Li

Journal of System Simulation

To address the challenges of performance degradation, high pilot overhead, and high computational complexity in traditi onal channel estimation methods for integrated sensing and communication (ISAC) assisted MIMO-OFDM systems when radar sensing information contains errors, this paper proposes a robust two-stage sparse channel estimation framework designed to be tolerant of sensing errors. In the first stage, a residual energy weighted simultaneous orthogonal matching pursuit (REW-SOMP) algorithm is designed. Leveraging locally adaptive dictionary expansion and a residual- weighted path selection mechanism, it accurately captures communication-associated paths even under sensing errors. The second stage introduces an adaptive penalty factor alternating direction method …

Dynamic Task Planning For Wargaming Based On Large Language Models, Yingang Liu, Ming Ma, Ronghua Zhang

Journal of System Simulation

To address the problems of great difficulty in intelligent decision-making and insufficient dynamism in task planning caused by the complex adversarial environment and strong uncertainty in wargaming tasks, this paper proposed a hierarchical Agent collaborative decision-making framework based on large and small model synergy.Through a multi-level structure, the hierarchical decoupling and dynamic coordination of battlefield tasks were achieved. A memory management module was constructed, and a query optimization mechanism driven by large language models was introduced to dynamically perceive the decision-making process and query intent, completing the semantic reconstruction and context completion of raw queries. A time-driven two-stage task …

Ultra-Short-Term Photovoltaic Power Prediction Based On Improved Patchtst Considering Data Drift, Huawei Mei, Penghui Yang, Yang Yu

Journal of System Simulation

Existing PV power prediction methods often suffer from limited accuracy and robustness due to three key shortcomings: relying on single-point mapping that cannot fully extract local temporal patterns; inadequate exploration of the global temporal dependencies in PV output, and failure to account for prevalent data drift phenomena. To overcome these limitations,an improved patch time series transformer (PatchTST) based approach is proposed for ultra-short-term PV power prediction. The methodology applies rough set theory for feature dimensionality reduction, effectively preserving critical decision information by analyzing both feature-label relationships and inter-feature correlations. An enhanced PatchTST model with a modified channel-independent mechanism extracts …

Sos Effectiveness Evaluation Method Based On Fuzzy Functional Dependency Network Analysis, Hongjia Su, Cheng Zhang, Fei Liu

Journal of System Simulation

Functional dependency network analysis (FDNA) enables modeling functional dependencies among equipment in a system of systems (SoS) and then computing the whole SoS effectiveness based on the effectiveness of each equipment, thus overcoming the deficiency of traditional SoS effectiveness evaluation based on tree-like index systems. However, critical parameters such as strength/criticality of dependency in this methodology currently rely on subjective empirical assignments, where the deviations resulting from subjectivity may compromise the accuracy of effectiveness evaluation. To address this limitation, this paper proposes a fuzzy FDNA (FFDNA)-based SoS effectiveness evaluation method. This method constructs a functional dependency network (FDN) model …

Multi-Agent Reinforcement Learning Method For Wargame Simulation Based On Suboptimal Demonstration Guidance, Zicong Zhou, Junjie Zeng, Yue Hu, Zhengqiu Zhu, Quanjun Yin

Journal of System Simulation

To address issues such as fixed behavior patterns and insufficient adaptability in complex adversarial environments exhibited by traditional wargame agent decision-making models, this paper proposes a multi-agent reinforcement learning method based on suboptimal demonstrations (MARLSD). The proposed method integrates reward relabeling with a self-imitation learning mechanism, effectively improving the training efficiency of multi-agent reinforcement learning algorithms in environments with large state-action spaces and sparse rewards, even when only a small number of suboptimal demonstrations are available, while encouraging agents to explore better strategies. Experimental results show that, compared with baselines such as QMIX and MAGAIL, MARLSD significantly improves performance and …

Research On Calculation Model Of Excavation Resistance Under Heterogeneous Soil Conditions, Xin Zhang, Ping Zhang, Chen Zhang, Wei Liu, Boyang Han

Journal of System Simulation

To address the problem of insufficient prediction accuracy of excavation resistance in heterogeneous cohesive soil, a spatial calculation model of excavation resistance at each excavation stage under heterogeneous soil conditions was proposedbased on the cutting wedge model, comprehensively considering multi-dimensional factors such as bucket geometry, side plate effect, lateral force, and inertia. By taking a small crawler hydraulic excavator as the research object, a coupled simulation model of boom multi-body dynamics and soil-rock particle discrete element was established, and the theoretical model was validated through the co-simulation of excavation operations. The simulation results indicate that under the working …

Research On Completion Method For Trajectory Based On Image Representation And Collaborative Feature Perception, Ye Tao, Jinhui Tang, Chen Zhou, Chong Wang

Journal of System Simulation

To address the constraints imposed by missing trajectory data in surveillance systems on the efficacy of civil aviation safety monitoring, as well as the limitations on the development and application of advanced technologies within trajectory-based operational frameworks, a completion method for trajectory based on image representation and collaborative feature perception was proposed. A conversion strategy for trajectory image representation was designed to reformulate the trajectory completion task as a deterministic image completion problem, effectively circumventing the cumulative error problem of traditional time-series data caused by the limitation of recurrent neural network inference mechanisms.A regression model fusing a multi-kernel hybrid …