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Multicriteria game approach to air-to-air combat tactical decisions for multiple UAVs

Journal of Systems Engineering and Electronics 2023年第6期34卷 1447-1464页

作者： JIANG Ruhao LUO He MA Yingying WANG Guoqiang School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization&Intelligent Decision-making Ministry of EducationHefei 230009China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei 230009China

Air-to-air combat tactical decisions for multiple unmanned aerial vehicles(ACTDMU)are a key decision-making step in beyond visual range *** influencing factors,strong antagonism and real-time requirements need to be considered in the ACTDMU *** this paper,we propose a multicriteria game approach to *** approach consists of a multicriteria game model and a Pareto Nash equilibrium *** this model,we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them,and we design the vector payoff functions based on predominance *** propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints(PNE-PRTC),and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium *** numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is *** on large-scale instances,the Pareto Nash equilibrium solutions can be calculated by PNEPRTC algorithm at the second *** simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.

关键词： tactical decision multicriteria game preference relation Pareto Nash equilibrium.

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A BSC-ANP Model for Evaluation of Strategic Fit of New Product Development Projects

A BSC-ANP Model for Evaluation of Strategic Fit of New Produ...

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第25届中国控制与决策会议

作者： Ying Yang ShanLin Yang Jian Yang School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

ISBN: (纸本)9781467355339

New product development (NPD) projects have been major forces driving company competitive advantage. All of them should align with corporate strategy so that the limited resources may be allocated effectively. However, few studies have been conducted to measure the extent that the NPD project matches with strategy. The paper identifies the cause-effect relationships between the strategy BSC measures for NPD projects based on strategy map and presents a BSC-ANP model to evaluate the strategic fit of projects. The causal relationships and interdependencies between projects and criteria are integrated with ANP model to produce the final priorities of projects with respect to their strategic fit. The proposed model was tested against empirical data drawn from NPD projects in an automobile company.

关键词： Evaluation, Strategic fit, New product development project, BSC-ANP

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A composite quantile regression long short-term memory network with group lasso for wind turbine anomaly detection

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2261-2274页

作者： Xu, Qifa Wu, Dongdong Jiang, Cuixia Wang, Xiangxiang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China

Wind turbine anomaly detection is an important but challenging work. Though deep learning is promising in this filed, it may fail for large error distributions and often lacks interpretability due to its complex structure. To this end, we develop a composite quantile regression long short-term memory network with group lasso (CQR-LSTM-GL). The CQR-LSTM-GL model extends the LSTM network to the quantile regression framework, which makes LSTM always valid regardless of the error distribution. Additionally, the CQR-LSTM-GL model adopts the group lasso method to compress the network structure and select key features. To illustrate its efficacy, we conduct a numerical experiment on the public server machine datasets. The experimental results show that the CQR-LSTM-GL model is not only slightly better than several competitive models in anomaly detection performance, but also far ahead in sparsity performance. The sparsity of CQR-LSTM-GL is 1.63 times that of CQR-LSTM-L1, and much more than that of CQR-LSTM. We then apply the CQR-LSTM-GL model to the anomaly detection of a wind turbine using the real Supervisory Control and Data Acquisition data, where all three anomalies are identified. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

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An Adaptive Local Grid Nesting-based Genetic Algorithm for Multi-earth Observation Satellites' Area Target Observation

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Journal of Systems Science and Systems Engineering 2024年第2期33卷 232-258页

作者： Ligang Xing Wei Xia Xiaoxuan Hu Waiming Zhu Yi Wu School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Inteligent Decision-making Ministry of EducationHefei 230009China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei 230009China

The Scheduling of the Multi-EOSs Area Target Observation(SMEATO)is an EOS resource schedul-ing problem highly coupled with computational *** advances in EOS technology and the ex-pansion of wide-area remote sensing applications have increased the practical significance of *** this paper,an adaptive local grid nesting-based genetic algorithm(ALGN-GA)is proposed for developing SMEATO ***,a local grid nesting(LGN)strategy is designed to discretize the target area into parts,so as to avoid the explosive growth of calculations.A genetic algorithm(GA)framework is then used to share reserve information for the population during iterative evolution,which can generate high-quality solutions with low computational *** this basis,an adaptive technique is introduced to determine whether a local region requires nesting and whether the grid scale is *** effectiveness of the proposed model is assessed experimentally with nine randomly generated tests at different *** results show that the ALGN-GA offers advantages over several conventional algorithms in 88.9%of instances,especially in large-scale *** fully demonstrate the high efficiency and stability of the ALGN-GA.

关键词： Multi-EOSs scheduling area target observation adaptive genetic algorithm local grid nesting

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Extreme Risk Measurement of Carbon Market Considering Multifractal Characteristics

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Journal of Systems Science & Complexity 2023年第6期36卷 2497-2514页

作者： ZHU Dandan ZHANG Chen PAN Di School of Management Hefei University of TechnologyHefei230009China School of Economics and Management Shanghai University of Political Science and LawShanghai201701China Key Laboratory of Process Optimization and Intelligent Decision-Making Hefei University of TechnologyHefei230009China

Influenced by the global economy,politics,energy and other factors,the price of carbon market fluctuates *** is of great practical significance to explore a suitable measurement method of extreme risk of carbon *** that the return series of carbon market has the characteristics of leptokurtosis,fat tail,skewness and multifractal,and there maybe many extreme risk values in the carbon market,this paper introduces the Skewed-t distribution which can describe the characteristics of leptokurtosis,fat tail and skewness of return series into MSM model which can describe multifractal characteristic of return series to model volatility of carbon *** the basis,based on the extreme value theory,this paper constructs Skewed-t-MSM-EVT model to measure extreme risk of carbon *** paper chooses EUA market as the object to study extreme risk of carbon market,and draws the following conclusions:Skewed-t-MSM-EVT model has significantly higher prediction accuracy for carbon market's Va R than MSM-EVT models under other distributions(including normal distribution,t distribution,GED distribution);Skewed-t-MSM-EVT model is superior to traditional Skewed-t-FIGARCH-EVT and Skewed-t-GARCH-EVT models in predicting carbon market's Va *** research has important practical significance for accurately grasping the risk of carbon market and promoting energy conservation and emission reduction.

关键词： Carbon market extreme value theory multifractal Skewed-t distribution VaR

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Independent or Social Driven Decision? A Counterfactual Refinement Strategy for Graph-Based Social Recommendation

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ACM Transactions on Information Systems 2025年第3期43卷

作者： Dongyang Li Jianshan Sun Chongming Gao Fuli Feng Kun Yuan School of Management Hefei University of Technology China School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education China University of Science and Technology of China Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) China University of Science and Technology of China China Hefei University of Technology Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management China

Social recommendation models have traditionally relied on social homophily to enhance user preference prediction by incorporating information from socially connected friends. However, this approach neglects the diverse nature of social relationships. Some individuals with independent personalities often prioritize their own interests over friends’ advice when making purchase decisions. Conversely, those who seek advice from others are more susceptible to social influence. Moreover, existing methods tend to overlook redundant and noisy social relationships within the network, hindering their ability to achieve accurate recommendations. In response, this paper proposes a novel counterfactual method to understand the causal factors driving purchase behaviors, thereby identifying the influence of users’ friends on their purchase decisions. By answering counterfactual questions about the influence of a friend's purchase behavior on the user's choices, we develop a causal model to represent social influence in the network. Our proposed refinement strategy, grounded in causal inference, generates counterfactual purchase behavior and guides the refinement of the social graph. Moreover, we present tailored graph refinement methods at various levels, ensuring fine-grained improvements. Experimental results on benchmark data demonstrate that the application of our strategy to different social recommendation models significantly enhances their predictive performance. The source code has been made available on https://***/LDY911/CFRSSR-Code.

关键词： social recommendation graph denoising causal inference

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Data-driven group decision making for diagnosis of thyroid nodule

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Science China(Information Sciences) 2019年第11期62卷 151-173页

作者： Chao FU Wenjun CHANG Weiyong LIU Shanlin YANG School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Department of Ultrasound The First Affiliated Hospital of USTC Division of Life Sciences and MedicineUniversity of Science and Technology of China

Emerging information technologies' integration into various fields has enhanced the development of these fields. Large volumes of data have been accumulated in this process. The accumulated data offer opportunities and challenges for people facing practical problems. On the one hand, it is essential to depend on a group's capabilities rather than an individual's capabilities to handle practical problems because the individual may lack sufficient expertise and experience to use data. In this situation, the practical problems can be considered as group decision making(GDM) problems. On the other hand, the accumulated data can help generate quality solutions to GDM problems. To obtain such solutions under the assumption that the accumulated data regarding a specific decision problem are available, this paper proposes a data-driven GDM method. In the method, decision makers' weights are learned from historical overall assessments and the corresponding gold standards, while criterion weights are learned from historical overall assessments and the corresponding decision matrices. The learned expert weights and criterion weights are used to produce the aggregated assessments, from which alternatives are compared or the overall conclusion is made. In a tertiary hospital located in Hefei, Anhui Province, China, the proposed method is applied to aid radiologists in diagnosing thyroid nodules.

关键词： data-driven group decision making interval number learning of expert weights learning of criterion weights diagnosis of thyroid nodule

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Earth observation satellite scheduling for emergency tasks

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Journal of Systems Engineering and Electronics 2019年第5期30卷 931-945页

作者： SUN Haiquan XIA Wei HU Xiaoxuan XU Chongyan School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of EducationHefei 230009China Graduate School Beijing Institute of Remote Sensing InformationBeijing 100192China

The earth observation satellites(EOSs)scheduling problem for emergency tasks often presents many *** example,the scheduling calculation should be completed in seconds,the scheduled task rate is supposed to be as high as possible,the disturbance measure of the scheme should be as low as possible,which may lead to the loss of important observation opportunities and data transmission *** scheduling algorithms are not designed for these ***,we propose a rolling horizon strategy(RHS)based on event triggering as well as a heuristic algorithm based on direct insertion,shifting,backtracking,deletion,and reinsertion(ISBDR).In the RHS,the driven scheduling mode based on the emergency task arrival and control station time window events are designed to transform the long-term,large-scale problem into a short-term,small-scale problem,which can improve the schedulability of the original scheduling scheme and emergency response *** the ISBDR algorithm,the shifting rule with breadth search capability and backtracking rule with depth search capability are established to realize the rapid adjustment of the original plan and improve the overall benefit of the plan and early completion of emergency ***,two heuristic factors,namely the emergency task urgency degree and task conflict degree,are constructed to improve the emergency task scheduling guidance and algorithm ***,we conduct extensive experiments by means of simulations to compare the algorithms based on ISBDR and direct insertion,shifting,deletion,and reinsertion(ISDR).The results demonstrate that the proposed algorithm can improve the timeliness of emergency tasks and scheduling performance,and decrease the disturbance measure of the scheme,therefore,it is more suitable for emergency task scheduling.

关键词： emergency task rolling horizon shifting rule backtrackingrule

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Solving the Observing and Downloading Integrated Scheduling Problem of Earth Observation Satellite with a Quantum Genetic Algorithm

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Journal of Systems Science and Information 2018年第5期9卷 399-420页

作者： Ye ZHANG Xiaoxuan HU Waiming ZHU Peng JIN School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling *** on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the *** results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.

关键词： Earth observation satellite integrated scheduling quantum genetic algorithm

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A hybrid multi-chromosome genetic algorithm for the cutting stock problem

A hybrid multi-chromosome genetic algorithm for the cutting ...

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International Conference on Information management, Innovation management and Industrial Engineering, ICIII

作者： Jin, Peng Zhang, Shu Chu School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Hefei China

ISBN: (纸本)9780769542799

This paper presents a hybrid multi-chromosome genetic algorithm (HMCGA) to solve an in integer linear programming formulation of the Cutting Stock Problem (CSP). The CSP is an important class combinatorial problem. It is appropriate to minimize the raw material used by industries for fulfilling customer's demands. In such cases, classic models for solving the cutting stock problem are useless. HMCGA differ from previous application of genetic algorithm to CSP in that our coding contain tow chromosome, one represents the cutting pattern and another represents the frequencies of the cutting patterns in the first chromosome, rather than relying on a traditional genetic algorithm to decode each individual solution. Results obtained from computational experiments for ten benchmarks demonstrate that the performance of HMCGA is compared to that obtained using existing mete-heuristic algorithm. © 2010 IEEE.

关键词： Genetic algorithms

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