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optimization of communication topology for persistent formation in case of communication faults

Chinese Physics B 2023年第7期32卷 610-624页

作者：王国强罗贺胡笑旋台建玮 School of Management Hefei University of TechnologyHefei 230009China Key Laboratory of Process Optimization&Intelligent Decision-making Ministry of EducationHefei 230009China Engineering Research Center for Intelligent Decision-making&Information Systems Technologies Ministry of EducationHefei 230009China

To address the optimization problem of communication topology for persistent formation in the case of communication faults such as link interruption,transmitter failure,and receiver failure a two-stage model including fast reconstruction of communication topology and re-optimization of communication topology is ***,a fast reconstruction algorithm of communication topology for persistent formation(FRA-CT-PF),based on optimally rigid graph,arc addition operation,and path reversal operation,is proposed,which can quickly generate a feasible reconstructed communication topology after communication faults,thus ensuring the safety of the agents and maintaining the formation shape of persistent ***,a re-optimization algorithm of communication topology for persistent formation(ROA-CTPF),based on agent position exchange,is proposed,which can further obtain a reoptimized communication topology to minimize the formation communication cost while still maintaining the formation shape of persistent *** time complexities of these two algorithms are also ***,the effectiveness of the above algorithms is verified by numerical *** with existing algorithms,FRA-CT-PF can always obtain feasible reconstructed communication topology in much less time under all communication fault scenarios,and ROA-CT-PF can obtain a reoptimized communication topology to further reduce the formation communication cost in a shorter time.

关键词： persistent formation communication topology formation communication cost communication fault

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

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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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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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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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The selection of project management software by FAHP and FMCDM in automobile R&D process

The selection of project management software by FAHP and FMC...

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2nd International Conference on Networking and Digital Society, lCNDS 2010

作者： Fu, Lei Shi, Li Yang, Ying Yu, Bengong Key Laboratory of Process Optimization and Intelligent Decision-making School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781424451616

The selection of project management (PM) software is a complex issue and has significant impacts to the efficiency of the automobile research & development (R&D) process. Given two alternatives based on critical path method (CPM) and critical chain project management (CCPM) respectively, decision-makers usually cause confusion. This paper analyzed the characteristic of automobile R&D project and developed an evaluation model based on the fuzzy analytic hierarchy process (FAHP) and fuzzy multiple criteria decision making (FMCDM) methods, which can help the managers in automobile industry to select more appropriate software in the course of product R&D. In particular, the FAHP method is used to obtain the weights of evaluation criteria, and the FMCDM method is used to determine the final rank of the software. The uncertainty and vagueness in evaluation procedure were presented as the fuzzy triangular numbers. The proposed method is applied to the case study of evaluating the performance of PM software based on CPM and CCPM respectively for a car manufacturer. The result of evaluation not only assisted managers in choosing suitable software but also outline the trend in development of CCPM software. ©2010 IEEE.

关键词： Managers

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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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Hierarchical method of task assignment for multiple cooperating UAV teams

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Journal of Systems Science & Complexity 2015年第5期28卷 1000-1009页

作者： Xiaoxuan Hu Huawei Ma Qingsong Ye He Luo School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education

The problem of task assignment for multiple cooperating unmanned aerial vehicle(UAV) teams is considered. Multiple UAVs forming several small teams are needed to perform attack tasks on a set of predetermined ground targets. A hierarchical task assignment method is presented to address the problem. It breaks the original problem down to three levels of sub-problems: target clustering, cluster allocation and target assignment. The first two sub-problems are centrally solved by using clustering algorithms and integer linear programming, respectively, and the third sub-problem is solved in a distributed and parallel manner, using a mixed integer linear programming model and an improved ant colony algorithm. The proposed hierarchical method can reduce the computational complexity of the task assignment problem considerably, especially when the number of tasks or the number of UAVs is large. Experimental results show that this method is feasible and more efficient than non-hierarchical methods.

关键词： unmanned aerial vehicle （UAV） task assignment clustering integer linear programming ant colony optimization（ACO） algorithm

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