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Two-phase node deployment for target coverage in rechargeable WSNs using genetic algorithm and integer linear programming

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JOURNAL OF SUPERCOMPUTING 2021年第4期77卷 4172-4200页

作者： Zameni, Mahsa Rezaei, Aref Farzinvash, Leili Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Node deployment is a matter of considerable concern in designing wireless sensor networks (WSNs). This paper studies this issue in the context of rechargeable WSNs (RWSNs). We propose an efficient algorithm, namely node deployment for target coverage in RWSNs (NDTCR), which determines the number and positions of installed sensors in two phases. The first phase applies genetic algorithm to construct a mesh over a subset of positions. The mentioned mesh covers the targets and connects them to the sink. In the second phase of NDTCR, we propose an integer linear programming (ILP) model to install some sensors at each position of the mesh. The advantage of applying the first phase is that it prunes the solution space considerably. Therefore, the proposed ILP model can be solved in a reasonable time. The experimental results demonstrate that NDTCR requires 29% fewer sensors on average in comparison with the previous approaches.

关键词： Node deployment Energy harvesting Rechargeable sensor network Genetic algorithm integer linear programming

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Virtual network embedding: ensuring correctness and optimality by construction using model transformation and integer linear programming techniques

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SOFTWARE AND SYSTEMS MODELING 2021年第4期20卷 1299-1332页

作者： Tomaszek, Stefan Speith, Roland Schuerr, Andy Tech Univ Darmstadt Real Time Syst Lab Merckstr 25 D-64283 Darmstadt Germany

Virtualization technology allows service providers to operate data centers in a cost-effective and scalable manner. The data center network (substrate network) and the applications executed in the data center (virtual networks) are often modeled as graphs. The nodes of the graphs represent (physical or virtual) servers and switches, and the edges represent communication links. Nodes and links are annotated with the provided and required resources (e.g., memory and bandwidth). The NP-hard virtual network embedding (VNE) problem deals with the embedding of a set of virtual networks to the substrate network such that (i) all (resource) constraints of the substrate network are fulfilled, and (ii) an objective is optimized (e.g., minimizing the communication costs). The existing two-step highly customizable model-driven virtual network embedding (MdVNE) approach combines model transformation (MT) and integer linear programming (ILP) techniques to solve the VNE problem based on a declarative specification. MdVNE generates element mapping candidates from an MT specification and identifies an optimal and correct embeddings using an ILP solver. In the past, developers created the MT and ILP specifications manually and needed to ensure carefully that both are compatible and respect the problem description. In this article, we present a novel construction methodology for synthesizing the MT and ILP specification from a given declarative model-based VNE problem description. This problem description consists of a metamodel for substrate and virtual networks, additional OCL constraints, and an objective function that assigns costs to a given model. This methodology ensures that the derived embeddings are correct w.r.t. the metamodel and the OCL constraints, and optimal w.r.t. the optimization goal. The novel model-based VNE specification is applicable to different network domains, environments, and constraints. Thus, the construction methodology allows to automate most of the ste

关键词： Data center Virtual network embedding Model-driven development integer linear programming Model transformation Graph transformation Triple-graph grammar Object Constraint Language

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Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming

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ENERGY REPORTS 2021年 7卷 283-291页

作者： Liu, Yu Liu, Congxiao Shen, Yiwen Zhao, Xin Gao, Shan Huang, Xueliang Southeast Univ Sch Elect Engn 2 Sipailou Nanjing 210018 Peoples R China Jiangsu Prov Key Lab Smart Grid Technol & Equipme Nanjing 210018 Peoples R China State Grid Nanjing Power Supply Co 1 Aoti St Nanjing 210019 Peoples R China

Home energy management system is proposed to reduce the influences caused by the high ratio penetration of renewable energy generation, through managing and dispatching the residential power and energy consumption in the demand side. Being aware of how the electric energy is consumed is a key step of this system. Non-intrusive Load Monitoring is regarded as the most potential method to address this problem, which aims to separate individual appliances in households by decomposing the total power consumption. In recent years, NILM is framed as a multi-label classification problem and many researches has been investigated in this field. In this paper, a non-intrusive method which can identify appliances power usage information from the total power consumption is proposed and thoroughly investigated. Firstly, the random k-labelset multi-label classification algorithm is enhanced by introducing random forest algorithm as base classifier. Then, grid search method and cross validation method are integrated to determine the optimal paraments set. This algorithm is used to achieve the appliances identification. Finally, based on the identification result, the integer linear programming is employed for power estimation of each appliance, especially multi-state appliances. Experimental results on low voltage networks simulator demonstrate that the proposed method has a high identification accuracy compared with the traditional random k-labelset multi-label classification methods with other base classifiers, and it is capable of identifying the power usages of different appliances accurately. The desirable performance of power estimation has broadened the applications of machine learning based non-intrusive energy monitoring. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Energy estimation Non-intrusive load monitoring Random k-labelset Random forest integer linear programming

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Topology optimization of fluid flow by using integer linear programming

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2021年第3期64卷 1221-1240页

作者： Souza, B. C. Yamabe, P. V. M. Sa, L. F. N. Ranjbarzadeh, S. Picelli, R. Silva, E. C. N. Univ Sao Paulo Escola Politecn Dept Mechatron & Mech Syst Engn Av Prof Mello Moraes 2231 BR-05508030 Sao Paulo Brazil Univ Sao Paulo Dept Min & Petr Engn Praca Narciso Andrade S-N BR-11013560 Santos SP Brazil

Topology optimization of fluid flow problems is still a challenging open problem, especially when considering turbulence, compressibility, or the addition of different physics. In the current implementation of topology optimization for fluids considering density methods, there are essentially three problems. First, the grayscale in the result makes it difficult to identify the precise contour of the fluid region, which may be a problem in some applications and during the optimization process as well. Second, even for low Reynolds flow design problems, a continuation scheme of the material model penalization parameters is necessary to avoid a grayscale and to obtain a clear boundary. Third, in complex fluid flow optimization problems, it is difficult to specify the maximum value of the inverse permeability to avoid the fluid to flow inside the solid. This work proposes a novel methodology that tackles the first two problems, i.e., it avoids the grayscale and obtains clear boundaries. The goal of this work is to implement the Topology Optimization of Binary Structures (TOBS) (Sivapuram and Picelli, Finite Elem Anal Des 139:49-61, 2018) for fluid flow design, which is a novel topology optimization method that has been used in solid mechanics to generate optimized structural solutions considering only binary {0,1} design variables. The main advantage of {0,1} methods is the clear definition of the interface and the absence of grayscale. It is a method easy to implement which preserves the material distribution features. Some classic fluid problems are considered to illustrate the problem, such as the double channel and the bend pipe, and also a more complex example that usually presents grayscale issues, which is the fluid diode design. The optimization results show the feasibility of the TOBS when applied to fluid flow problems. The physical problem is solved by using the finite element method and the optimization problem with CPLEX (c), a proprietary optimization pack

关键词： Topology optimization Fluid flow Navier-Stokes flow TOBS method integer linear programming

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Edge metric dimensions via hierarchical product and integer linear programming

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OPTIMIZATION LETTERS 2021年第6期15卷 1993-2003页

作者： Klavzar, Sandi Tavakoli, Mostafa Univ Ljubljana Fac Math & Phys Ljubljana Slovenia Univ Maribor Fac Nat Sci & Math Maribor Slovenia Inst Math Phys & Mech Ljubljana Slovenia Ferdowsi Univ Mashhad Fac Math Sci Dept Appl Math POB 1159 Mashhad 91775 Razavi Khorasan Iran

If S = {v(1),..., v(k)} is an ordered subset of vertices of a connected graph G and e is an edge of G, then the vector r(G)(e vertical bar S) = (d(G)(v(1), e),..., d(G)(v(k), e)) is the edge metric S-representation of e. If the vertices of G have pairwise different edge metric S-representations, then S is an edge metric generator for G. The cardinality of a smallest edge metric generator is the edge metric dimension edim(G) of G. A general sharp upper bound on the edge metric dimension of hierarchical products G(U) Pi H is proved. Exact formula is derived for the case when vertical bar U vertical bar = 1. An integer linear programming model for computing the edge metric dimension is proposed. Several examples are provided which demonstrate how these two methods can be applied to obtain the edge metric dimensions of some applicable graphs.

关键词： Metric dimension Edge metric dimension Hierarchical product integer linear programming Molecular graph

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A Novel integer linear programming Formulation for Job-Shop Scheduling Problems

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IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 5937-5944页

作者： Liu, Anbang Luh, Peter B. Yan, Bing Bragin, Mikhail A. Tsinghua Univ Dept Automat Ctr Intelligent & Networked Syst CFINS Beijing 10084 Peoples R China Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Rochester Inst Technol Dept Elect & Microelect Engn Rochester NY 14623 USA

Job-shop scheduling is an important but difficult problem arising in low-volume high-variety manufacturing. It is usually solved at the beginning of each shift with strict computational time requirements. To obtain near-optimal solutions with quantifiable quality within strict time limits, a direction is to formulate them in an integer linear programming (ILP) form so as to take advantages of widely available ILP methods such as Branch-and-Cut (B&C). Nevertheless, computational requirements for ILP methods on existing ILP formulations are high. In this letter, a novel ILP formulation for minimizing total weighted tardiness is presented. The new formulation has much fewer decision variables and constraints, and is proven to be tighter as compared to our previous formulation. For fast resolution of large problems, our recent decomposition-and-coordination method "Surrogate Absolute-Value Lagrangian Relaxation" (SAVLR) is enhanced by using a 3-segment piecewise linear penalty function, which more accurately approximates a quadratic penalty function as compared to an absolute-value function. Testing results demonstrate that our new formulation drastically reduces the computational requirements of B&C as compared to our previous formulation. For large problems where B&C has difficulties, near-optimal solutions are efficiently obtained by using the enhanced SAVLR under the new formulation.

关键词： Manufacturing job-shop scheduling integer linear programming decomposition and coordination

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Schema Compliant Consistency Management via Triple Graph Grammars and integer linear programming

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FORMAL ASPECTS OF COMPUTING 2021年第6期33卷 1115-1145页

作者： Weidmann, Nils Anjorin, Anthony Paderborn Univ Paderborn Germany IAV GmbH Ingenieurgesell Auto & Verkehr Berlin Germany

In the field of Model-Driven Engineering, Triple Graph Grammars (TGGs) play an important role as a rule-based means of implementing consistency management. From a declarative specification of a consistency relation, several operations including forward and backward transformations, (concurrent) synchronisation, and consistency checks can be automatically derived. For TGGs to be applicable in realistic application scenarios, expressiveness in terms of supported language features is very important. A TGG tool is schema compliant if it can take domain constraints, such as multiplicity constraints in a meta-model, into account when performing consistency management tasks. To guarantee schema compliance, most TGG tools allow application conditions to be attached as necessary to relevant rules. This strategy is problematic for at least two reasons: First, ensuring compliance to a sufficiently expressive schema for all previously mentioned derived operations is still an open challenge;to the best of our knowledge, all existing TGG tools only support a very restricted subset of application conditions. Second, it is conceptually demanding for the user to indirectly specify domain constraints as application conditions, especially because this has to be completely revisited every time the TGG or domain constraint is changed. While domain constraints can in theory be automatically transformed to obtain the required set of application conditions, this has only been successfully transferred to TGGs for a very limited subset of domain constraints. To address these limitations, this paper proposes a search-based strategy for achieving schema compliance. We show that all correctness and completeness properties, previously proven in a setting without domain constraints, still hold when schema compliance is to be additionally guaranteed. An implementation and experimental evaluation are provided to support our claim of practical applicability.

关键词： Model-driven engineering Triple graph grammars integer linear programming Graph constraints Application conditions

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Topology optimization applied to the acoustic medium inverse problem in the time domain using integer linear programming

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2023年第4期66卷 78-78页

作者： Moreira, Joao B. D. Goncalves, Juliano F. Sivapuram, Raghavendra Carmo, Bruno S. Silva, Emilio C. N. Univ Sao Paulo Sch Engn Dept Mechatron & Mech Syst Engn Sao Paulo SP Brazil Dassault Syst Simulia Corp Johnston RI USA Univ Sao Paulo Sch Engn Dept Mech Engn Sao Paulo SP Brazil

In this paper, the acoustic inverse problem modeled in the time domain featuring wave velocity reconstruction in the presence of sharp interfaces is addressed using an integer design variable approach. The medium being reconstructed is assumed piecewise constant, with single-material obstacles embedded in a homogeneous background. The wave equation is modeled using the Finite Element Method (FEM). The inversion procedure aims at finding the parameter field that minimizes a least-squares misfit function with respect to data generated from a synthetic model. The proposed optimization methodology is based on a sequential integer linear programming (ILP) formulation used in the field of Topology Optimization (TO). Since this is a gradient-based technique, the sensitivity with respect to the integer design variable is evaluated by the adjoint method. Sensitivities are modified using both damping filters and Helmholtz-type Partial Differential Equation (PDE) filters to deal with the ill-posedness that is inherent to this class of inverse problem. The integer design variable is binary, associating each point of the domain either to the homogeneous background or to the embedded obstacles. This description naturally incorporates the sharp interface hypothesis, whereas a continuous design variable may generate transition regions with intermediate values and no clearly defined boundary. The damping filter is successful in controlling instabilities by incorporating the whole optimization history to the design update. Furthermore, the generality and effectiveness of the proposed framework are evaluated by addressing 2D problems from the literature and a proposed 3D case, all featuring sharp interfaces.

关键词： Topology optimization Acoustic inverse problem Time domain integer linear programming Finite element method

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Enhancing Course Planning with BERT-Infused GAT Embeddings and integer linear programming

Enhancing Course Planning with BERT-Infused GAT Embeddings a...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Luis Olmos Adam Kaplan Maryam Jalalitabar Department of Computer Science California State University Northridge Northridge USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Integrating BERT-augmented Graph Neural Networks (GNNs) with integer linear programming (ILP) provides a novel framework for academic course recommendation and scheduling. Our approach enriches node representations with BERT embeddings to capture the semantic context of course descriptions. These representations are further refined using supervised contrastive loss within the GNN to enhance the understanding of course relationships and dependencies, leading to more accurate and context-aware recommendations. The framework incorporates an ILP scheduler that optimizes course sequences under academic constraints, including prerequisites, elective requirements, major-specific pathways, and semester unit limits. This method supports the creation of personalized multi-semester schedules tailored to students' academic goals, such as double majors or major-minor combinations. Evaluated on synthetic data, the approach demonstrates improved alignment with academic pathways, showcasing its potential to bridge the gap between semantic understanding and effective course planning.

关键词： Schedules Accuracy Semantics Machine learning Contrastive learning integer linear programming Graph neural networks Optimization Synthetic data Testing

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Designing fast-charge urban electric bus services: An integer linear programming model

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2023年第1期171卷

作者： Battaia, Olga Dolgui, Alexandre Guschinsky, Nikolai Kovalyov, Mikhail Y. KEDGE Business Sch Bordeaux France CNRS IMT Atlantique LS2N Nantes France Natl Acad Sci Belarus United Inst Informat Problems Minsk BELARUS

Currently, there is serious political support for the decarbonization of transport locally, nationally and even internationally. Public transport operators are focusing on the use of electric buses as an opportunity to reduce greenhouse gas emissions and improve air quality. However, using electric buses requires a functional infrastructure of urban charging points. Fast-charging can be made available thanks to the progress made on the major technological charging devices in recent years. In this study, we consider an optimization problem of the design of an infrastructure for a fast-charge city electric bus service. The decisions which have to be made include determining a mixed fleet of conventional and electric buses, points for electric chargers and power stations, quantities of charging plug devices, a distribution of electric buses between the routes, and matching chargers with power stations. The objective is to maximize the route-weighted total passenger capacity of electric buses. An integer linear programming model has been developed to complement the existing non-linear model. The new model is efficient if the number of possible charging spots is small, which is natural and frequent in practice. Extensive computer experiments demonstrate that our approach delivers near-optimal solutions of the studied problem in ten minutes for real-world instances on a standard PC and it outperforms the earlier approach on every instance.

关键词： Electric bus Fast-charging Charging infrastructure Optimization integer linear programming

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