New Jersey Institute of Technology
College of Civil Engineering and Architecture, Xinjiang University, Urumqi 830047, China
University of Nebraska-Lincoln
University of South Florida
Northern Illinois University
คณะวิศวกรรมศาสตร์
Florida Institute of Technology
Evolving Ai Integration In Complex Medical Decision-Making And Multidisciplinary Transplant Care: A Systematic Review Of Human-Ai Collaboration,
2026
Missouri University of Science and Technology
Evolving Ai Integration In Complex Medical Decision-Making And Multidisciplinary Transplant Care: A Systematic Review Of Human-Ai Collaboration, Rachel L. Dzieran, Cihan H. Dagli, Robert J. Marley
Engineering Management and Systems Engineering Faculty Research & Creative Works
Purpose of Review: Artificial intelligence (AI) in healthcare has evolved dramatically from early expert systems, which were initially considered replacements for clinical judgment, to today's collaborative frameworks that aim to augment physician decision-making. This evolution is particularly crucial in domains such as transplant surgery, where decisions carry irreversible consequences and require the integration of complex, often ambiguous data. Drawing on peer-reviewed literature from 2019 to 2025, we conducted a systematic review that analyzed key elements distinguishing successful human-AI partnerships from those that fail. Recent Findings: The ideal balance incorporates human expertise into AI systems through weighted integration approaches, rather than …
Llm-Driven Simulation Method For Mobilization Decision-Making In Weapons And Equipment Supply Chain,
2026
School of Management, Beijing Institute of Technology, Beijing 100081, China
Llm-Driven Simulation Method For Mobilization Decision-Making In Weapons And Equipment Supply Chain, Qichang Guo, Jihai Zhang, Zichu Qin
Journal of System Simulation
Abstract: A LLM-driven multi-agent simulation model for production mobilization in the weapons and equipment supply chain is developed to address the practical challenge of transitioning the supply chain from peacetime to wartime production in response to surging wartime demand. The model consists of a military demand layer, a supply chain execution layer, and a mobilization decision-making layer. An LLM is incorporated as a mobilization decision generation module to represent the dynamic regulation of production conversion and expansion under war-readiness budget and production safety constraints. The simulation results show that compared with the no-mobilization and rule-based mobilization strategies, the LLM-based strategy …
Chemical Plant Control Based On Dynamic Simulation And Reinforcement Learning,
2026
Beijing East Simulation & Control Technology Co., Ltd., Beijing 100029, China; East Simulation Technology (Beijing) Co., Ltd., Beijing 100029, China; North China Electric Power University, Beijing 102206, China
Chemical Plant Control Based On Dynamic Simulation And Reinforcement Learning, Yuxin Li, Haijie Li, Shangbo Han, Kai Gao, Qingyuan Zhang, Yanwen Xiao, Zhengcai Liu
Journal of System Simulation
Abstract: An intelligent control strategy framework combining offline training and on-site deployment for regenerative thermal oxidizer (RTO) operation control based on RL is proposed to solve the problem that fluctuations in exhaust gas concentration and flow, as well as changes in working conditions, make it difficult for RTO to maintain the system operating parameters within the economically optimal range in practical applications. In the offline phase, RL is employed to enable autonomous policy learning and global optimization. In the online phase, an adaptive policy correction mechanism based on real-time data is introduced. RL control strategy is integrated with the operation …
Five-Dimensional Digital Twin System For Closed-Loop Gasoline Blending Optimization,
2026
College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
Five-Dimensional Digital Twin System For Closed-Loop Gasoline Blending Optimization, Yaning Fang, Renchu He
Journal of System Simulation
Abstract: An adaptive blending optimization system based on five-dimensional digital twin theory is designed and developed to address the challenges of strict nonlinear constraints and frequent raw material fluctuations. A five-dimensional model architecture is constructed to achieve virtual-real mapping and synergy throughout the blending process. A closed-loop control strategy of "online model correction and dynamic recipe optimization" is proposed. The constrained recursive least squares (RLS) algorithm is adopted to update the octane blending effect model parameters, resolving the model mismatch caused by raw material fluctuations. The linear decreasing weight particle swarm optimization algorithm (LDW-PSO) is combined to determine the optimal …
Research On Digital Shipyard Design-Procurement Coupling Based On Multi-Agent Systems,
2026
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Research On Digital Shipyard Design-Procurement Coupling Based On Multi-Agent Systems, Kaitai Peng, Dingyi Feng, Wenbo Zhao, Feier Chen
Journal of System Simulation
Abstract: To address the issues of low supply chain collaboration efficiency and inadequate dynamic scheduling capability caused by the disconnection between design and procurement in digital shipbuilding, a method integrating multi-agent simulation with the Q-learning reinforcement learning algorithm is proposed, and a four-tier dynamic supply chain model coupling design and procurement is developed, covering mines, steel mills, shipyards, and shipowners. Based on the depth of collaboration, steel mills are classified into three levels, including full collaboration, partial collaboration, and noncollaboration, with differentiated state spaces and reward functions designed to simulate intelligent pricing and game behaviors under information asymmetry. Simulation results …
Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems,
2026
System Engineering Research Institute, Academy of Military Science, Beijing 100101, China
Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems, Long Zhang, Wenbo Huang, Xuanming Feng, Zhen Lei, Ying Wang
Journal of System Simulation
Abstract: A hierarchical evolutionary adaptive learning (HEAL) framework is proposed to address the problems in the operational simulation of intelligent equipment systems under complex adversarial environments, including fragmented hierarchical decision-making, unclear mapping between algorithmic indicators and operational effectiveness, and the lack of adaptive evolution capability of simulation models. A multi-layer learning architecture that integrates robust autonomous decision-making for single units, emergent collaboration of system networks, and game-theoretic evolution of system-of-systems strategies is constructed by following the “micro-meso-macro” system-oriented hierarchical modeling paradigm. A cross-layer closed-loop coupling among learned policies, cooperative structures, and operational effectiveness is achieved by combining bottom-up behavioral abstraction …
Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments,
2026
State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science & Technology, Huainan 232001, China; School of Artificial Intelligence, Anhui University of Science & Technology, Hefei 231131, China
Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments, Dequan Li, Jiahao Jiang
Journal of System Simulation
Abstract: To address the requirements for low-latency local planning and safe obstacle avoidance in UAV forest-fire patrol under dense tree-trunk occlusion and locally invalid depth observations, this paper proposes a learning-based local trajectory planning method that fuses depth images, local occupancy distance maps, candidate-level collision-risk assessment, and pre-execution safety reranking. The method uses depth images, local occupancy distance maps, motion states, and target direction as inputs and generates 15 quintic-polynomial candidate trajectories in parallel using 3 × 5 angular motion primitives. During training, candidate safety costs, ranking supervision, and near-collision risk labels are constructed. During inference, online LiDAR point clouds …
Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm,
2026
School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm, Xuezhi Yue, Chao Xiong, Yanshen Jiang, Zhaolu Guo, Hu Peng
Journal of System Simulation
Abstract: To address prediction failure, manifold topology distortion, and insufficient adaptability in environmental response and operator configuration in existing prediction-based algorithms for dynamic multi-objective optimization under complex nonlinear environments and small-sample settings, this paper proposes a manifold alignment and dual prediction-based adaptive dynamic multi-objective evolutionary algorithm, termed MADP-DMOEA. When the environment changes, the algorithm decomposes the decision-variable manifold into center and shape components, effectively aligns manifolds at adjacent time instants via bipartite optimal matching, and identifies environmental change severity using geometric displacement. A dual prediction model is developed, in which a GRNN performs center extrapolation under severe changes, while an …
Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg,
2026
Naval Submarine Academy, Qingdao 266000, China
Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg, Yonghao Zhang, Changyou Xue, Xueman Fan, Zhuo Cheng
Journal of System Simulation
Abstract: A cooperative hunting method based on improved multi-agent deep deterministic policy gradient (MADDPG) is proposed to address the challenges of non-stationary environments, sparse rewards, and partial observations in the cooperative hunting of targets with unknown motion patterns by multiple unmanned surface vessels (USVs). A Markov game model for hunting is built, and a multidimensional guided reward shaping function incorporating collision avoidance, hunting success, and energy consumption penalties is designed to mitigate the sparse reward problem. ResNet residual networks are introduced to construct the Actor and Critic networks and thus enhance the representation capability of deep networks for complex state-action …
Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning,
2026
Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China
Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning, Junjie Liu, Jianlei Xu, Litao Ju, Yanhai Hu, Qinghao Zhang, Qiang Liu
Journal of System Simulation
Abstract: A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, …
Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application,
2026
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China
Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application, Xin Zhang, Han Wang, Pengjiang Qian, Wei Fang
Journal of System Simulation
Abstract: A dynamic multi-objective optimization algorithm based on residual-driven two-stage prediction (RDTP) is proposed to address the dilemma of prediction strategies in dynamic multi-objective optimization struggling with insufficient samples in the early stage and low accuracy in the later stage. In the initial stage, if an environmental change is detected, grey prediction, which is fast and suitable for small samples, is employed to generate individuals for the new environment. A residual analysis is performed to determine whether to switch to a LSSVM to improve prediction accuracy as more samples become available. The Pareto front is clustered to select detection objects. …
Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response, Ding Ma, Tao Zhang, Bo Jiang, Yajie Liu, Shuijia Li
Journal of System Simulation
Abstract: In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rational allocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based …
Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters,
2026
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters, Renxin Xiao, Yifan Xu, Guisheng Chen, Nan Pan
Journal of System Simulation
Abstract: A two-level optimization model integrating heterogeneous electric vehicle fleet configuration with multimodal path coordination is built to address the challenges in industrial cluster freight transportation involving electric heterogeneous fleet scheduling, such as diverse tasks and stringent constraints on energy consumption and delivery timeliness. Considering the performance coupling characteristics of heterogeneous electric fleets in terms of load-energy consumption-endurance, based on the coordination of multimodal paths and fleet configuration coding, the upper level employs the GWO to optimize task splitting and fleet configuration. The lower level adopts a multi-neighborhood SA algorithm to achieve path coordination scheduling. Simulation results show that the …
Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules,
2026
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules, Hualan Wang, Li Wang, Zhongning Fu
Journal of System Simulation
Abstract: To address the limitations of circular RGV systems under high-density operating conditions, including a simple operational structure and limited parallel operation capability, this study investigates track structures and dispatching mechanisms for complex operational requirements. Based on the agent-based modeling method implemented on the AnyLogic platform, a simulation model of an RGV system with a nested circular track layout was developed, in which an embedded sub-loop equipped with track-switching devices is introduced into the traditional circular track structure. Operating and dispatching rules suitable for the nested circular structure are designed by incorporating the concepts of section operation and overtaking in …
Air Defense Resource Allocation And Reconstruction Method Based On Kggom,
2026
Beijing Institute of Electronic System Engineering, Beijing 100854, China
Air Defense Resource Allocation And Reconstruction Method Based On Kggom, Jiaoxiong Chen, Chenglong Zhang, Yuqiang Zhao, Xianglin Li, Qiang Zhao
Journal of System Simulation
Abstract: To address the challenge that traditional solving methods cannot meet real-time requirements in the intelligent allocation and reconstruction of air defense operation resources, this paper focuses on an intelligent solving method for the dynamic allocation and reconstruction of intercept timing sensor-weapon-target. A multi-constraint model is established with damage effectiveness and operation cost as optimization objectives, and a knowledge-guided genetic optimization method (KGGOM) is proposed. Through knowledge-guided population generation and evolution, constraint-aware operators with correction mechanisms, and multi-path search combined with simulated annealing for gene correction, it improves its optimization capability and speed. Historical superior solution retention and dynamic elite …
Hybrid Jaya-Transformer-Based Task Scheduling Algorithm For Super-Agile Satellites,
2026
School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Hybrid Jaya-Transformer-Based Task Scheduling Algorithm For Super-Agile Satellites, Haoyu Zhao, Guanghui Zhang, Lanting Hao, Meijia Guo
Journal of System Simulation
Abstract: Super-agile satellite task scheduling is characterized by complex constraints and tight coupling among task transitions. To address the relatively long online solution times of traditional heuristic algorithms, this paper establishes a super-agile satellite task scheduling model that aims to maximize the task profit ratio while considering constraints on visible time windows, attitude transition time, energy, and storage capacity. A Transformer is used to learn task requirements, visible time windows, and global correlations among tasks, and a dynamic mask and a task scheduler are incorporated to generate feasible task sequences. To enhance the model's ability to learn complex task correlations …
Aerial Refueling Scheduling Plan Generation Method For Multi-Position Task Management *,
2026
Air Force Engineering University, Xi'an 710038, China
Aerial Refueling Scheduling Plan Generation Method For Multi-Position Task Management *, Jiachen Hu, Huanyu Li
Journal of System Simulation
Abstract: An aerial refueling scheduling plan generation method based on hierarchical hybrid strategy is proposed to address the high complexity of aerial refueling scheduling for multi-position tasks and the difficulty for traditional manual operations or single-position-oriented solutions to manage tasks effectively. A multi-level strategic framework based on fixed-rule heuristics, rolling horizon optimization, and steady-state cyclic scheduling is developed and integrated into a hierarchical hybrid strategy to balance scheduling efficiency, optimization capability, and emergency response capability. Simulation results demonstrate that the proposed method effectively generates feasible refueling scheduling plans, producing viable plans under both undisturbed rotational operations and disturbed dynamic support …
Flight Test Mission Planning Method Based On Adaptive Large Neighborhood Search,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Flight Test Mission Planning Method Based On Adaptive Large Neighborhood Search, Luofu Wu, Xinyang Wu, Lunhao Ju, Guopeng Song, Rui Wang, Jiang Jiang
Journal of System Simulation
Abstract: Flight test mission planning is critical to aircraft performance evaluation, but traditional manual methods often struggle with extensive solution spaces and complex constraints. This paper constructs an overall flight test mission planning model and introduces a flight test planning algorithm (FTPA) based on adaptive large neighborhood search. FTPA utilizes Kahn topological sorting to generate an initial test-item priority sequence, which is then decoded into an initial solution, and jointly schedules flight test items and test aircraft within the adaptive large neighborhood search framework. A comparison with the CPLEX solver shows that the FTPA algorithm yields solutions with a gap …
Large Language Model-Assisted Control Framework For Mixed Remote Sensing Satellite Constellations,
2026
School of Automation, Central South University, Changsha 410083, China
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,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; State Key Laboratory of Space System Operation and Control, Xi'an 710043, China
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 …
