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A nonlinear integer programming Approach to Solving the Robust Parameter Design Optimization Problem

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2016年第8期32卷 2859-2870页

作者： Ozdemir, Akin Cho, Byung Rae Clemson Univ Dept Ind Engn Clemson SC 29631 USA Clemson Univ Ind Engn Clemson SC 29631 USA

Robust parameter design is a widely implemented design methodology for continuous quality improvement by identifying optimal factor level settings with minimum product variation. However, apparent flaws surrounding the original version of robust parameter design have resulted in alternative approaches, of which response surface methodology using the central composite design, in particular, has drawn a great deal of attention. There is a large number of practical situations in which some or all of variables must be integers;however, the design space associated with the traditional central composite design is typically a bounded convex feasible set involving real numbers. The purpose of this paper is twofold. First, we discuss why the Box-Behnken design may be preferred over the central composite design and other three-level designs when maintaining constant or nearly constant prediction variance associated with a second-order model is crucial to integer-valued robust parameter design problems. Second, we lay out the foundation to show how the Box-Behnken design is transformed into a nonlinear integer programming framework. In this paper, we develop Box-Behnken design embedded nonlinear integer programming models, using the sequential quadratic integer programming and the Karush-Khun-Tucker conditions. Comparison studies of the proposed models and traditional counterparts are also conducted. It is believed that the proposed models have the potential to impact a wide range of engineering problems, ultimately leading to process and quality improvement. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： Box-Behnken design response surface methodology nonlinear integer programming optimization

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A Novel TRUST-TECH Guided Branch-and-Bound Method for nonlinear integer programming

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2015年第10期45卷 1361-1372页

作者： Chiang, Hsiao-Dong Wang, Tao Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

nonlinear integer programming has not reached the same level of maturity as linear programming, and is still difficult to solve, especially for large-scale systems. Branch-and-bound (B&B) and its variants are widely used methods for integer programming, and numerical solutions obtained by them still can be far away from the global optimum. In this paper, we propose a novel approach to guide the deterministic/heuristic methods and the commercial solvers for nonlinear integer programming, and aim at improving the solution quality by taking advantage of transformation under stability-retraining equilibrium characterization (TRUST-TECH) method. Moreover, we examine the effectiveness by developing and simulating TRUST-TECH guided B&B and TRUST-TECH guided commercial solver(s), and compare their performance with that of the original methods/solvers (e.g., GAMS (General Algebraic Modeling System)/BARON, GAMS/SCIP, and LINDO (Linear, INteractive, Discrete Optimizer)/MINLP) and also with that of recentlyreported evolutionary-algorithm (EA)-based methods. Simulation results provide evidence that, the solution quality is substantially improved, and the global-optimal solutions are usually obtained after the application of TRUST-TECH. The proposed approach can be immediately utilized to guide other EA-based methods and commercial solvers which incorporate intelligent searching components.

关键词： Branch-and-bound (B&B) nonlinear integer programming transformation under stability-retraining equilibrium characterization (TRUST-TECH)

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Solving a class of two-stage stochastic nonlinear integer programs using value functions

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JOURNAL OF GLOBAL OPTIMIZATION 2025年第1期91卷 129-153页

作者： Zhang, Junlong Oezaltin, Osman Y. Trapp, Andrew C. Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA Worcester Polytech Inst WPI Business Sch Worcester MA 01609 USA

We propose a level-set characterization of the value function of a class of nonlinear integer programs with finite domain. We study the theoretical properties of this characterization and show the equivalence between the set of level-set minimal vectors and a set of non-dominated right-hand side vectors. We use these properties to develop a solution approach for two-stage nonconvex integer programs with stochastic right-hand sides. The proposed approach can solve problems with pure integer variables in both stages. We demonstrate the effectiveness of the proposed approach using a nonlinear generalized assignment problem with uncertain capacity. We also conduct computational experiments using two-stage quadratically-constrained quadratic integer programs with stochastic right-hand sides. The proposed value function-based approach can solve instances whose extensive forms are among the largest stochastic quadratic integer programs solved in the literature with respect to the number of rows and variables in extensive form, and with considerably more rows.

关键词： nonlinear integer programming Value function Stochastic programming Generalized assignment problem

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LEO satellites selection-based computation offloading algorithm in aircraft-satellite multi-access edge computing networks

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COMPUTER COMMUNICATIONS 2024年 223卷 115-127页

作者： Zhang, Jiadong Zhang, Ruidong Shi, Wenxiao Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

The emergence of sixth -generation (6G) mobile networks and non -terrestrial networks (NTNs) has led to increased interest in low Earth orbit (LEO) satellite -based communication networks for their potential to provide global coverage and ubiquitous connectivity. In this paper, we investigate the LEO satellites selectionbased computation offloading problem in the aircraft-satellite multi-access edge computing (ASMEC) network, where LEO satellites and edge computing processors are integrated to provide ubiquitous and low -latency communication and computation services for aircraft during flights. In contrast to most existing works, which directly assume a fixed number of satellites or orbits in satellite -based MEC networks, we investigate the problem of how many satellites and which satellites to select in the ASMEC network. Our objective is to minimize the average total time delay of tasks during aircraft-satellite computation offloading. To achieve this, we formulate a nonlinear integer programming (NLIP) problem and propose the LEO satellites selectionbased computation offloading (LSSBCO) algorithm to solve it, which includes the shortest aircraft-satellite distance based access satellite selection (ASS -SD) algorithm and the nearest k ( t ) neighboring satellites selection (NSS- k ( t ) ) algorithm. We evaluate the performance of the LSSBCO algorithm in terms of the average total time delay, the maximum throughput, the average aircraft-satellite distance, the average connection duration, and the number of satellite handovers. Numerical results show that the proposed algorithm outperforms the benchmark algorithms with a lower average total time delay.

关键词： Aircraft-satellite communication Computation offloading LEO satellites selection Multi-access edge computing nonlinear integer programming Queuing system

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Multi-task entity linking with supervision from a taxonomy

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KNOWLEDGE AND INFORMATION SYSTEMS 2023年第10期65卷 4335-4358页

作者： Wang, Xuwu Chen, Lihan Zhu, Wei Ni, Yuan Xie, Guotong Yang, Deqing Xiao, Yanghua Fudan Univ Sch Comp Sci Shanghai Peoples R China Shanghai Key Lab Data Sci Shanghai Peoples R China East China Normal Univ Sch Comp Sci Shanghai Peoples R China Pingan Hlth Tech Shanghai Peoples R China Fudan Univ Sch Data Sci Shanghai Peoples R China

Entity linking is the task of resolving ambiguous mentions in documents to their referent entities in a knowledge graph (KG). Existing solutions mainly rely on three kinds of information: local contextual similarity, global coherence, and prior probability. But the information of the mentions' types is rarely utilized, which is helpful for precise entity linking. That is to say, if the type information of a mention is obtained from a mention classifier, we can exclude candidate entities with different types. However, the key challenge of realizing it lies in obtaining the type labels with appropriate granularity and performing entity linking with the error propagated from the mention classifier. To solve the challenges, we propose a model named type-oriented multi-task entity linking (TMTEL). First, we select types with appropriate granularity from the taxonomy of a KG, which is modeled as a nonlinear integer programming problem. Second, we use a multi-task learning framework to incorporate the selected types into entity linking. The type information is used to enhance the representation of the mentions' context, which is more robust to the errors of the mention classifier. Experimental results show that our model outperforms multiple existing solutions.

关键词： Entity linking Taxonomy Mention classification Knowledge graph Multi-task learning nonlinear integer programming

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Effectiveness of Aircraft Inter-Arrival Control in Upstream Traffic Flow via a Combined Tandem Fluid Queue Model and integer programming Approach

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IEEE ACCESS 2023年 11卷 15252-15270页

作者： Higasa, Koki Sekine, Katsuhiro Itoh, Eri Univ Tokyo Dept Aeronaut & Astronaut Tokyo 1138656 Japan Tokyo Univ Sci Dept Informat & Comp Technol Tokyo 1628601 Japan Elect Nav Res Inst Air Traff Management Dept Tokyo 1820012 Japan

Conventionally, several studies indicated that controlling aircraft arrival time in the en-route airspace mitigates arrival aircraft congestion in the terminal airspace. Further research is required to clarify how to leverage this idea to design an air traffic management system, a so-called Extended Arrival MANager (E-AMAN), to reduce the arrival traffic flow while assisting air traffic controllers and boosting their effectiveness quantitatively. Under these circumstances, this research proposed aircraft inter-arrival time control within the en-route airspace and clarified its effectiveness in reducing arrival delay based on mathematical modeling and simulation evaluations. In this paper, we developed the G(t)/GI/s(t) + GI tandem fluid model to analyze the time-varying delay time of flights in both en-route and terminal airspace and demonstrated the effect of inter-arrival time control in the upstream arrival traffic flow in the en-route airspace, combining the model with the nonlinear integer programming problem. The calculation results for 3,074 aircraft over 21 days, arriving at Tokyo International Airport between 17:00 and 22:00, show the possibility for the control to reduce the mean and maximum delay time for flights by 18.8% (5.0 s) and 16.5% (37.6 s) on average within the en-route airspace. Moreover, fast-time simulation by AirTOP is conducted to validate the control, revealing the scope to reduce mean and maximum delay times in the terminal airspace by 11.5% (36.5 s) and 19.2% (148.8 s) on average.

关键词： Atmospheric modeling Delays Aircraft Air traffic control Airports Analytical models nonlinear systems Air traffic management arrival management nonlinear integer programming tandem fluid model

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Diversified Software Deployment for Long-Term Risk Mitigation in Cyber-Physical Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第1期37卷 377-387页

作者： Zhang, Zhimei Huang, Shaowei Chen, Ying Li, Boda Mei, Shengwei Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Since modern cyber-physical power systems are vulnerable to coordinated wide-area cyber attacks, it is necessary to mitigate the potential risk as much as possible. At the planning stage, the defender can utilize software diversity, which is a common phenomenon that the cyber software of different substations comes from different competing vendors. Therefore, different kinds of software may not be exposed to the same zero-day security loophole, preventing the attacker from taking charge of multiple substations at the same time. In this paper, the optimal scheme of software deployment considering long-term risk mitigation is studied. Firstly, the framework of diversity-based cyber defense against malicious attacks is formulated. Secondly, the risk index based on representative attack patterns is constructed, which is the objective to be minimized. Thirdly, considering that the deployment scheme is long-term stable while the operating mode varies with time, we construct a multiobjective nonlinear stochastic programming to mitigate the average risk of operating modes. Then the optimization problem is solved by the multiobjective genetic algorithm. Lastly, results of the IEEE 39-node CPPS and and the Virtual European Grid demonstrate that the proposed method can considerably reduce the attack risk.

关键词： Substations Cyberattack Software Power systems Indexes Security Risk management Cyber-physical power system multiobjective genetic algorithm (MOGA) nonlinear integer programming risk mitigation stochastic programming software diversity

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Dynamic Gaussian mutation beetle swarm optimization method for large-scale weapon target assignment problems

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APPLIED SOFT COMPUTING 2024年 162卷

作者： Xu, Han Zhang, An Bi, Wenhao Xu, Shuangfei Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rulebased chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency.

关键词： Weapon target assignment Swarm intelligence optimization algorithm Beetle swarm optimization nonlinear integer programming

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Amplitude-optimized Koopman-linear flow estimator for wind turbine wake dynamics: Approximation, prediction and reconstruction

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ENERGY 2023年第PartE期263卷

作者： Chen, Zhenyu Lin, Zhongwei Ren, Xin Chen, Kaixuan Zhang, Guangming Xie, Zhen Wang, Chuanxi She, Chao China Huaneng Grp Co Ltd Beijing 100031 Peoples R China North China Elect Power Univ Sch Control & Comp Engn State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China Huaneng Clean Energy Res Inst Beijing 102209 Peoples R China Huairou Lab Beijing 101400 Peoples R China China Power Engn Consulting Grp Cent Southern China Elect Power Design Inst Co Ltd Wuhan 430064 Peoples R China

The low-order modeling of wind turbine dynamic wake is a challenging problem, which requires approximating high-dimensional, nonlinear dynamic systems with a limited number of states and linear structures. The Koopman-linear flow estimator is regarded as a promoting method in this area to approximate the dynamic wake field from a few physical-measured states. This paper presents optimization methods for the Koopman-linear flow estimator to improve its generalization applicability and modeling accuracy in specific applications. Firstly, the flow estimator's wake field prediction process is organized using Koopman modes and amplitudes;both are identified initially. Then, a optimization method is proposed to optimize the Koopman amplitudes for a given application scenario, which maintains a consistent form for uncontrolled and controlled systems based on the error-source analysis. After this, a sequential particle swarm optimization algorithm is adopted, which improves the computationally-intensive problem during sensor configuration optimization. The proposed algorithm quickly solves a feasible and optimized sensor configuration plan instead of the unavailable global -optimal one. The verification results show two conclusions: On the one hand, the nonlinearity during the yaw-induced wake deflection process is evident, which poses a significant challenge to the linear low-order approximation of the dynamic wake field. On the other hand, the proposed optimization methods highly improve the dynamic wake modeling accuracy under free-wake and yaw-controlled scenarios. Sparse sampling is necessary for dynamic wake behavior study in industrial. This paper solves the incomplete measurements and wake deflection nonlinearity problem caused by sparse sampling and promotes the generalization applicability for specific applications.

关键词： Flow reconstruction Wake Dynamic mode decomposition Koopman mode Optimal Koopman amplitudes nonlinear integer programming

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Improvement of network robustness against cascading failures based on the min-max edge-adding strategy

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2023年 611卷

作者： Ma, Shan Shen, Binda Ma, Junfeng Hu, Wenfeng Peng, Tao Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

This paper explores the improvement of the network robustness against cascading failures by edge addition. Several edge-adding strategies are compared, including the random edge-adding strategy, the high betweenness centrality edge-adding strategy, the high Pagerank centrality strategy and the min-max edge-adding strategy. In the min-max edge-adding strategy, the problem for robustness improvement of complex networks is formulated as a nonlinear integer programming problem, and an algorithm is proposed to solve this problem. From the numerical experiments, we find that the min- max edge-adding strategy is more effective in improving the network robustness against cascading failures than the other strategies. Finally, this work can provide guidance for the design of complex networks to prevent potential cascading failures. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Complex network Edge addition Robustness improvement Cascading failure nonlinear integer programming

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