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Dynamic Performance Evaluation of High-Valued Complex Systems Based on Belief Rule Base

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Can Feng, Zhichao Zhou, Zhijie Gao, Yongjia PLA Rocket Force Univ Engn Coll Missle Engn Xian 710025 Shaanxi Peoples R China

This study presents a novel dynamic method for evaluating the performance of high-valued complex systems (HVCSs). It aims to address the following three problems in performance evaluation (PE) of this type of system, including limited observation data of system states, uncertainty in expert knowledge, and high real-time requirement. To address these challenges, we expand the discernment frame of the belief rule base (BRB) to the power set (BRB-P), enhancing its capacity to handle limited data and uncertain expert knowledge. The belief rules are integrated using the evidential reasoning (ER) algorithm, ensuring the interpretability of the framework. Simultaneously, we introduce a new optimization model to enhance the real-time performance of the framework and mitigate the explosion of belief rule combinations when dealing with multiple input features. Furthermore, we develop a utility-based framework reduction method to adaptively adjust the discernment framework of BRB-P. This enables the structure and parameters of the framework to be dynamically trained using real-time observation data. An experimental illustration is presented to demonstrate the effectiveness of the proposed framework.

关键词： Performance evaluation Rockets Servomechanisms Data models Real-time systems Complex systems Accuracy Uncertainty optimization models Noise Belief rule base (BRB) dynamic performance evaluation (PE) framework reduction method high-valued complex systems (HVCSs) power set

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The Three-Stage Strategy of Bi-Level Optimal Energy Management in the Distribution-Home Network Based on Golf optimization Algorithm

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IEEE ACCESS 2024年 12卷 183973-183990页

作者： Goodarzi, Javad Askari, Mohammad Tolou Amirahmadi, Meysam Babaeinik, Majid Islamic Azad Univ Dept Elect Engn Semnan Branch Semnan Iran

This paper introduces a comprehensive three-stage strategic framework designed to enable home microgrids (H-MGs) to collaborate effectively within multiple interconnected electrical and thermal grids. By forming coalitions, H-MGs can enhance their competitiveness in the energy market. The framework is supported by a bi-level optimization model that addresses the optimal management of both electrical and thermal energy within home microgrids, while also integrating electricity and heat distribution networks. A key aspect of this model is the incorporation of a specialized demand-side management strategy, which focuses on solving the optimization problem to maximize the overall system's profit, despite the presence of variable uncertainties. The bi-level optimization model operates on two levels. The upper-level model focuses on maximizing the profit of the network operator, taking into account Combined Heat and Power (CHP) resources. The lower-level model, on the other hand, is designed to minimize the cost of electricity supply for H-MGs. To solve this complex optimization problem, the paper proposes a Multi-Stage Stochastic Programming approach based on the Golf optimization Algorithm (MSSP-GOA). The effectiveness of the proposed method is demonstrated through a detailed simulation study. The results show that the method significantly reduces the market clearing price (MCP) for approximately 26% of the time intervals. Additionally, it leads to a 42% increase in the consumption of responsive loads within H-MGs and a threefold increase in local generation. The MSSP-GOA algorithm not only enhances market participation but also significantly boosts profits for all participants involved, underscoring its potential as a robust solution for optimizing energy management in collaborative microgrid environments.

关键词： Microgrids optimization Costs Energy management Uncertainty Collaboration Cogeneration Renewable energy sources optimization models Thermal energy Home energy management uncertainty evolutionary algorithm bi-level optimization electric/thermal grids

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Research on the Adjustment and optimization of Train Working Diagrams Including Tourist-Dedicated Trains

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IEEE ACCESS 2024年 12卷 145740-145752页

作者： Cui, Yuanding Li, Yinzhen Ma, Changxi Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

Because China's high-speed railway diverts many passengers from the existing railway, the existing railway network capacity has greatly increased, which provides convenient conditions to begin tourist-dedicated train projects. Due to the lack of research on the optimization of train working diagrams, including tourist-dedicated trains, first, the time characteristics of tourist-dedicated train passengers are analyzed. Then, according to the proposed optimization principle of train working diagram under the condition of tourist-dedicated train operation, the optimization model is constructed, the operation line of dedicated train is added to the original operation diagram, and the optimization is adjusted. The validity of the model is verified by examples and comparative experiments. The research results show that the constructed train working diagram optimization model and algorithm can adjust and optimize the running lines of dedicated trains and has good practicability. This study fills the gap in the research of this problem and makes a theoretical innovation in the theory of train working diagram optimization, and can provide a theoretical basis for the operation planning, transportation organization and other process optimization of subsequent tourist-dedicated train projects of railway transportation management departments.

关键词： Rail transportation optimization Costs Genetic algorithms Safety optimization models Planning Delays High-speed rail transportation Tourist-dedicated train train working diagram adjustment and optimization time characteristics double objective optimization

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optimization on Multimodal Network Considering Time Window Under Uncertain Demand

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Bei, Honghan Lin, Han Yang, Fangjiao Li, Xiaolong Murcio, Roberto Yang, Tianren Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Dalian Maritime Univ Collaborat Innovat Ctr Transport Studies Dalian 116026 Peoples R China COSCO SHIPPING Logist & Supply Chain Management Co Dalian 116001 Peoples R China Birkbeck Univ London Sch Social Sci London WC1E 7HX England UCL Ctr Adv Spatial Anal London W1T 4TJ England Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China

Improving transport efficiency is challenging for multimodal transport participants to improve cost-effectiveness. This paper proposes to select city nodes and establish a multi-objective fuzzy optimization model with mixed time window constraints to consider customer demand and transportation time uncertainty. T-rex optimization algorithm (TROA) is used to solve the problem, which efficiently lowers transportation costs and carbon emissions and has higher precision and dependability than Particle Swarm optimization (PSO) and Genetic Algorithm (GA). The efficacy of this method is proven using the example of the multimodal transportation network in China's central-eastern economic zone. These findings provide potential solutions for multimodal transportation aimed at enhancing transportation efficiency.

关键词： Transportation optimization Uncertainty Planning Costs Routing Carbon dioxide optimization models Logistics Biological system modeling Clustering algorithm mixed time windows multimodal transport network optimization uncertain demand

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Identification of Joint Strategic Bidding Considering Spatial Correlation of Electricity Prices

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2414-2426页

作者： Zhu, Zhirun Yang, Zhifang Yu, Juan Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment Technol Chongqing 400044 Peoples R China

Prices in electricity market exhibit tight spatial correlations due to transmission constraints. Bidding deviations at one bus can significantly influence prices at others, which shows the high potential of strategic bidding to obtain excessive profits. However, this property is not considered in related studies regarding the identification of joint strategic bidding. To address this issue, this paper first formulates the spatially correlated strategic bidding problem as a bilevel optimization problem. The key challenge for identifying spatially correlated strategic bidding is that the number of solutions required for the bilevel optimization calculation is a combinatorial function of the scale of market participants. Theoretical derivation shows that the joint profit considering spatial correlations can be decomposed into separate profits within a neighborhood. Then, we present a criterion for effective decomposition. A practical bound of the deviation incurred by such decomposition is provided. Utilizing these properties, a computationally efficient identification method of the spatially correlated strategic bidding is presented. The combinations of market participants that most likely to acquire excessive joint profit are listed. Numerical experiments indicate that the proposed method is capable of effectively identify the spatially correlated strategic bidding potentials with an accuracy of over 97%, while notably accelerating the efficiency.

关键词： Electricity Correlation Electricity supply industry Indexes optimization Electric potential Costs optimization models Accuracy Vectors Strategic bidding electricity market spatial correlation of electricity prices fast identification method locational marginal price (LMP)

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System Restoration for Low-Inertia Power Systems Incorporating Fast Frequency Response via Distributionally Robust optimization

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第3期40卷 2230-2243页

作者： Qin, Zhijun Li, Yunming Chen, Xinwei Liu, Hui Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

The high share of renewable energy sources (RESs) in power system creates inertia shortfalls, posing challenges in system restoration after a major outage due to lower system inertia and high RES uncertainty. In this paper, the restorability of low-inertia power systems is studied. A rolling horizon methodology is derived to construct restoration strategies by performing sequential decision-making to optimize blackstart, grid energization, wind power re-connection, and load pickup. Notably, wind power re-connection will introduce time-varying exogenous uncertainty for other restoration actions. To decouple exogenous uncertainty, a bi-level optimization model is built, where the upper-level determines the set of non-blackstart units and increment of grid-accommodable wind capacity, considering Minimum System Inertia Constant (MSIC) and Fast Frequency Response (FFR). The lower-level yields a secure operating point ensuring power balance and System Non-Synchronous Penetration (SNSP). The former is formulated as a look-ahead deterministic optimization model, while the latter as a distributionally robust optimization model considering wind uncertainty. Steady-state and dynamics simulation using the IEEE 14-bus system and 118-bus system are provided, demonstrating that RES can perform system restoration safeguarded by MSIC and SNSP, and FFR can accelerate restoration by providing extra inertia support for systems with up to 50% share of wind capacity.

关键词： Uncertainty Wind power generation Wind farms optimization Security Power system stability Load modeling Frequency synchronization Frequency response optimization models System restoration low-inertia power systems fast frequency response distributionally robust optimization

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Energy Consumption Modeling and Process Parameter optimization of Internal Gear Power Honing Under Multi-Axis Coupling

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 14510-14524页

作者： Zhang, You Li, Congbo Tang, Ying Cao, Huajun Tao, Guibao Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

The internal gear power honing process is widely used in gear machining for electric vehicles because of the advantages of high-precision and high-efficiency machining. The gear honing process involves six axes and high spindle speeds, this process contributes a substantial amount of energy consumption but has less attention on energy saving. To improve the energy efficiency of gear honing process, this paper proposes an energy consumption modeling and process parameter optimization method under multi-axis coupling. The multi-axis coupling motion and energy consumption characteristics of gear honing process are analyzed. The energy consumption model of gear honing process under multi-axis coupling is then established, and the influence law of process parameters on honing energy consumption is investigated. Furthermore, a multi-objective process parameter optimization model for minimizing energy consumption and machining time is constructed. An improved multi-objective atomic orbital search (IMOAOS) algorithm is developed to solve the multi-objective optimization problem. The gear honing experiment results demonstrate that the optimized scheme reduces energy consumption by 39.75% and machining time by 8.48% compared to the empirical scheme. The proposed multi-objective optimization scheme also significantly balances the energy consumption and machining time of gear honing process compared with single-objective optimization.

关键词： Energy consumption Gears optimization Wheels Couplings Milling Machine tools Turning optimization models Feeds Energy consumption modeling process parameter optimization internal gear power honing multi-axis coupling

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Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年第2期42卷 165-188页

作者： Oksuz, Elif Yilmaz, Omer Faruk Oksuz, Mehmet Kursat Yilmaz, Beren Gursoy Karadeniz Tech Univ Dept Ind Engn TR-61080 Trabzon Turkiye Univ Florida Ctr Appl Optimizat Dept Ind & Syst Engn Gainesville FL USA Erzincan Binali Yildirim Univ Dept Comp Engn Erzincan Turkiye Univ Florida Dept Ind & Syst Engn Supply Chain & Logist Engn Lab Gainesville FL USA

Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

关键词： Disassembly line balancing problem reverse supply chain design optimization models distribution strategies

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Engineering-Adaptive Pavement Maintenance Decision-Making Model: A Reinforcement Learning Approach From Expert Feedback

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Cai, Wenyuan Du, Yuchuan Wu, Difei Weng, Zihang Liu, Chenglong Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai 201800 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

The increase in highway mileage and lifespan is driving up the demand for road maintenance. With most research focusing on corrective maintenance, remedial maintenance(such as sealing and patching) optimization is understudied. Oriented toward remedial maintenance, data-driven models often fall short due to difficulty in establishing and implementing the model under complex road conditions, while the experts' decision lacks consistency amidst multifaceted factors. To address this gap, this paper proposes a fine-grained maintenance decision model that combines data-driven methods with expert knowledge through Reinforcement Learning from Expert Feedback (RLEF). The experts' experience introduced in decision-making model could improve the engineering application ability of decisions. The research uses a pavement performance prediction model as the environment and applies reinforcement learning to optimize strategies in the decision model. Additionally, the model integrates multidimensional expert feedback into reward functions to better understand ambiguous decision rules. Real-world data validation demonstrates that the RLEF model can adapt to engineering scenarios and applications better as well as achieve superior cost-effectiveness.

关键词： Maintenance Roads Decision making Reinforcement learning Costs optimization models Predictive models Adaptation models Accuracy Linear programming Pavement maintenance decision-making reinforcement learning from expert feedback fine-grained reward model data-knowledge-driven

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Burst Traffic Demand Based Adaptive Network Planning and optimization Approach for 5G/6G Network

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IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 883-887页

作者： Guo, Yiyun Xie, Yaqin Nanjing Univ Informat Sci & Technol Changwang Sch Honors Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China

To support the long-term stable communication demand along with burst traffic demands in 5G/6G smart environments, a new hybrid deployment strategy for fixed base stations (BSs) and unmanned aerial vehicles (UAVs) is proposed. Firstly, a cell integration strategy based on user distribution is proposed, which addresses the issues of high algorithm complexity and poor stability associated with existing hybrid macro/micro BS deployments. Secondly, to tackle the problem of large-scale burst traffic demand that cannot be solely covered by fixed BSs, UAVs are deployed according to user traffic demand, thereby effectively enhancing network coverage and minimizing unnecessary overheads. Simulation results reveal that our approach enhances coverage by 38.45%, reduces algorithm complexity by 31.22%, and decreases UAV deployment costs by 8.9% compared to the traditional method, while ensuring over 98% coverage of burst traffic demand. Furthermore, it confirms that our method fulfills the requirements of mobile user scenarios.

关键词： Autonomous aerial vehicles Signal to noise ratio optimization Interference Planning optimization models Clustering algorithms Disasters Costs Complexity theory Adaptive network planning burst traffic demand 5G/6G network optimization

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