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检索条件"主题词=mixed-integer linear programming"

共 2269 条记录，以下是621-630 订阅

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MIP Outer Belief Approximations of Lower Conditional Joint CDFs in Statistical Matching Problems 16th

MIP Outer Belief Approximations of Lower Conditional Joint ...

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16th International Conference on Scalable Uncertainty Management, SUM 2024

作者： Baioletti, Marco Capotorti, Andrea Petturiti, Davide Vantaggi, Barbara University of Perugia Perugia Italy Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031762345

We propose a mixed integer programming (MIP) procedure to find an outer belief approximation of a lower conditional joint cumulative distribution function (lower conditional joint CDF) obtained by the statistical matching of several sources of information, given a common variable. We assume that the variables have finite supports and we provide a procedure based on the MIP technique that produces a sparse solution with at most a given finite number of focal elements, permitting to obtain an outer approximation with a conditional belief function. In turn, the family of sparse solutions given the common variable, allows us to efficiently perform coherent inferences on new items, relying on the generalized Bayesian conditioning rule. We finally show the effectiveness of the proposed approach in the domain of company fraud detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mixed-integer linear programming

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Hybrid Genetic Algorithms and Heuristics for Nonlinear Short-Term Hydropower Optimization: A Comparative Analysis 28th

Hybrid Genetic Algorithms and Heuristics for Nonlinear Short...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Aminabadi, M. Jafari Séguin, S. Fofana, I. Department of applied sciences Université du Québec à Chicoutimi SaguenayQcG7H 2B1 Canada Department of computer science and mathematics Université du Québec à Chicoutimi SaguenayQcG7H 2B1 Canada GERAD MontréalQcH3T 1J4 Canada Canada

In this paper, a mixed integer Nonlinear programming (MINLP) for the short-term hydropower optimization problem considering operational constraints such as demand and startup costs, is presented. Since solving the MINLP is complicated and, in many cases, impossible, three methods are proposed based on reducing the complexity, which is hybridized with the exact solver. Method A, a binary genetic algorithm;method B, an iterative heuristic method;and method C, using the iterative heuristic method in the genetic algorithm. Based on computational results in a case study, method B converges to a solution very quickly and with few iterations, whereas methods A and C perform more efficiently. A comparison between methods A and C indicates that method C not only reduces the computational burden for convergence but also yields better results. The proposed methods are evaluated by comparing them with optimal solutions. The results indicate that the proposed methods are highly effective in achieving favorable results. © 2024 The Authors.

关键词： mixed-integer linear programming

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Detecting and Exploiting Unit Commitment Structures in MIPs 6

Detecting and Exploiting Unit Commitment Structures in MIPs

引用

6th International Conference on Power and Energy Technology, ICPET 2024

作者： Su, Zhao-Gang Chen, Sheng-Jie Li, Yu-Fei Chen, Liang Kou, Caixia Cai, Zhi Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China College of Sciences Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China

ISBN: (纸本)9798350375855

The unit commitment (UC) problem has been extensively researched in the literature, which is typically formulated as a mixed integer programming (MIP) problem. However, current studies lack effective methods to identify potential UC problem structures within a general MIP problem, making it challenging to leverage well-established valid inequalities for UC problems to accelerate the solution process. In this paper, we present a novel detection method that automatically identifies UC structures within an MIP problem by analyzing the relationships between variables and constraints. Moreover, we enhance the MIP formulation by incorporating three well-studied classes of valid inequalities as cutting planes, based on the identified UC structures. Our experiments demonstrate that our proposed UC detection method greatly improves computational efficacy of a standard MIP solver compared to the default setting. © 2024 IEEE.

关键词： mixed-integer linear programming

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Optimizing Renewable Energy Community Management Through Multi-Objective Approaches 24

Optimizing Renewable Energy Community Management Through Mul...

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24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Thomopulos, Dimitri D'Ambrosio, Claudia Raugi, Marco Destec Università di Pisa Pisa Italy Unesco chair on 'Sustainable Energy Communities' University of Pisa Pisa Italy Lix Cnrs École Polytechnique Institut Polytechnique de Paris Palaiseau France

ISBN: (纸本)9798350355185

Multi-objective optimization plays a significant role in optimizing the sizing and operation of Renewable Energy Communities (RECs), facilitating informed decision-making through precise Pareto curves. In this study, we extend a model to incorporate thermal loads and explore the effectiveness of the A-AUGMECON2 algorithm and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for solving it. Through comparative analysis, we aim to assess the performance and robustness of different optimization approaches in enhancing the sustainability and efficiency of REC operation. © 2024 IEEE.

关键词： mixed-integer linear programming

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Reliability Modeling and Redundancy Allocation for Systems of Dependent Components Using Copula Functions

Reliability Modeling and Redundancy Allocation for Systems o...

引用

IISE Annual Conference and Expo 2024

作者： Li, Zhanhang Coit, David W. Department of Industrial & System Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States

ISBN: (纸本)9781713877851

This study introduces a novel approach for redundancy allocation in series-parallel systems, incorporating component dependency via copula functions. By modeling these dependencies, we enhance the accuracy of system reliability assessment. Our methodology employs mixed-integer nonlinear optimization, balancing system reliability with constraints like cost and weight. In practical applications, the condition and reliability of components within the same system are influenced by similar external factors or by the condition of each other components, potentially leading to dependencies in their operational state and reliability assessments. When component reliability models are applied to complex systems composed of multiple dependent elements, the observed reliability behaviors tend to be more detailed and complicated. This complexity necessitates the adoption of novel perspectives to accurately evaluate and manage system reliability. We applied two distinct methodologies to solve the mixed-integer nonlinear optimization problem for our series-parallel model: interior-points nonlinear optimization and genetic algorithms. Interior-points method proved effects in efficiently approximating the complex relationship between decision variables and the objective function and provided an upper-bound on system reliability. The genetic algorithm excels at solving integer programming problems and delivers a robust and practical optimal redundancy strategy for these challenges. © IISE Annual Conference and Expo *** rights reserved.

关键词： mixed-integer linear programming

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The Dial-a-Ride problem with meeting points: A problem formulation for shared demand–responsive transit

The Dial-a-Ride problem with meeting points: A problem formu...

引用

作者： Cortenbach, L.E. Gkiotsalitis, K. van Berkum, E.C. Walraven, E. Department of Civil Engineering University of Twente Drienerlolaan 5 Enschede7522 NB Netherlands Department of Transportation Planning and Engineering National Technical University of Athens Iroon Polytechniou 5 Athens15773 Greece Anna van Buerenplein 1 The Hague2595 DA Netherlands

In this paper, a formulation for the Dial-a-Ride Problem with Meeting Points (DARPmp) is introduced. The problem consists of defining routes that satisfy trip requests between pick-up and drop-off points while complying with time window, ride time, vehicle load, and route duration constraints. A set of meeting points is defined, and passengers may be asked to use these meeting points as alternative pickup or drop-off points if this results in routes with lower costs. Incorporating meeting points into the DARP is achieved by formulating a mixed-integer linear program. Two preprocessing steps and three valid inequalities are introduced, which improve the computational performance when solving the DARPmp to global optimality. Two versions of the Tabu Search metaheuristic are proposed to approximate the optimal solution in large-scale networks due to the NP-hardness of DARPmp. Performing numerical experiments with benchmark instances, this study demonstrates the benefits of DARPmp compared to DARP in terms of reducing vehicle running costs. © 2024 Elsevier Ltd

关键词： mixed-integer linear programming

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Coordination of Autonomous Vehicles using a mixed-integer LPV-MPC Planner 63

Coordination of Autonomous Vehicles using a Mixed-Integer LP...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Samada, Sergio E. Puig, Vicenc Nejjari, Fatiha Sarrate, Ramon Universitat Politènica de Catalunya Advanced Control Systems Group Barcelona Spain

ISBN: (纸本)9798350316339

This work addresses the problem of coordinating multiple autonomous vehicles. In particular, an optimal planner based on Model Predictive Control (MPC) is designed for each vehicle, using the linear parameter-varying (LPV) representation of each vehicle model to provide feasible references that ensure constraint satisfaction. Meanwhile, mixed integer linear inequalities are embedded in the optimization problem to ensure collision avoidance. The proposed approach is evaluated in an aggressive driving scenario using a 1/10 scale electric car. © 2024 IEEE.

关键词： mixed-integer linear programming

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Temporal and spatial coordinated optimization of integrated energy systems under uncertain conditions 4

Temporal and spatial coordinated optimization of integrated ...

引用

4th International Conference on Intelligent Power and Systems, ICIPS 2024

作者： Zhang, Xiaoxin Lu, Yuguo Jiangnan University School of Internet of Things Engineering Wuxi China Anhui Technical College of Mechanical and Electrical Engineering School of Management Wuhu China

ISBN: (纸本)9798331532765

This paper adopts a min-max-min two-layer robust optimization method to deal with the inherent uncertainty of renewable energy power generation units in integrated energy systems. First, the principles of wind power and photovoltaic power generation are analyzed to point out the sources of uncertainty. Then, the uncertainty set of renewable energy power generation is constructed, and the model affected by uncertainty is divided into two layers of models, inner and outer. On this basis, the dual method is used to transform the initial two-layer model into a mixed integer linear programming, and finally the C & CG method is used for iterative solution. Finally, the analysis is carried out on the matlab platform in the form of an example, and the impact of different uncertain parameters on the system economy is discussed. © 2024 IEEE.

关键词： mixed-integer linear programming

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AMPA: Adaptive mixed Precision Allocation for Low-Bit integer Training 41

AMPA: Adaptive Mixed Precision Allocation for Low-Bit Intege...

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41st International Conference on Machine Learning, ICML 2024

作者： Ding, Li Fei, Wen Huang, Yuyang Ding, Shuangrui Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Information Engineering The Chinese University of Hong Kong Shatin NT Hong Kong

Low-bit integer training emerges as a promising approach to mitigate the heavy burden during network training by quantizing the weights, activations, and gradients. However, existing methods cannot well achieve mixed-precision quantization for low-bit training and are commonly limited to INT8 precision. In this paper, we propose a novel low-bit integer training framework that, for the first time, achieves adaptive mixed-precision allocation (AMPA) for weights, activations, and gradients, and pushes the boundaries to a precision level below INT8. We develop a novel magnitude-based sensitivity measurement with regard to the quantization losses of weight, activation, and gradient quantization and the average gradient magnitudes, which is demonstrated as an upper bound of quantization influence in theory. We further design a layer-wise precision update strategy under observations on the quantization losses and their effects on model performance in low-bit training. Extensive experiments on different backbones and datasets show that, compared to INT8 quantization, the proposed method can achieve more than 38% BitOPs reduction with a tolerable loss below 2% in image classification, image segmentation, and language modeling. Copyright 2024 by the author(s)

关键词： mixed-integer linear programming

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Data-driven Optimal Scheduling of Isolated Microgrid Using Random Forest Regressor 24

Data-driven Optimal Scheduling of Isolated Microgrid Using R...

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24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Zandrazavi, Seyed Farhad Shafie-Khah, Miadreza Pashaei, Meysam School of Technology and Innovations University of Vaasa Vaasa Finland

ISBN: (纸本)9798350355185

Design, operation, and planning of Microgrids (MGs) have been enriched by advances in machine learning (ML) techniques and availability of real data. These advancements have significantly improved the accuracy of predictions related to energy demand and renewable generation within MGs. ML-based algorithms can analyze complex patterns in data to make precise forecasts about future energy demands, renewable energy output, and even potential system failures. In this context, this paper utilizes real open-access wind speed dataset from Nottingham to create a forecast model based on a random forest regressor (RFR). This forecasted data, along with actual wind speed for wind power generation, serves as the input for a mixed-integer linear programming model designed for the optimal scheduling of standalone microgrids (OSSM). The results demonstrate that the discrepancies in the total cost, voltage deviation, and power loss of the MG, when comparing forecasted data to actual data, are less than 5%, 1%, and 2%, respectively. These results validate the effectiveness of ML-based models, specifically RFR, in the development of OSSM. © 2024 IEEE.

关键词： mixed-integer linear programming

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