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

共 2269 条记录，以下是581-590 订阅

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Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization 63

Spectral Graph Theoretic Methods for Enhancing Network Robus...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Somisetty, Neelkamal Nagarajan, Harsha Darbha, Swaroop Texas A & M University Department of Mechanical Engineering College StationTX United States Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350316339

This paper addresses the optimization of edge-weighted networks by maximizing algebraic connectivity to enhance network robustness. Motivated by the need for precise robot position estimation in cooperative localization and pose-graph sparsification in Simultaneous Localization and Mapping (SLAM), the algebraic connectivity maximization problem is formulated as a mixed integer Semi-Definite Program (MISDP), which is NP-hard. Leveraging spectral graph theoretic methods, specifically Cheeger's inequality, this work introduces novel 'Cheeger cuts' to strengthen and efficiently solve medium-scale MISDPs. Further, a new mixed integer linear Program (MILP) is developed for efficiently computing Cheeger cuts, implemented within an outer-approximation algorithm for solving the MISDP. A greedy k-opt heuristic is also presented, producing high-quality solutions that serve as valid lower bounds for Cheeger cuts. Comprehensive numerical analyses demonstrate the efficacy of strengthened cuts via substantial improvements in run times on synthetic and realistic robot localization datasets. © 2024 IEEE.

关键词： mixed-integer linear programming

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A global optimal trajectory optimization method with multiple NFZ constraints 1

A global optimal trajectory optimization method with multipl...

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1st Aerospace Frontiers Conference, AFC 2024

作者： Chen, Yang Liang, Zhuo Lyu, Rui China Academy of Launch Vehicle Technology Beijing1000762 China

ISBN: (纸本)9781510681613

The trajectory optimization challenge, especially with multiple no-fly zones (NFZs), often leads to many local optima. Using mixed-integer programming (MIP) improves chances for finding global optima but at the cost of increased computation due to many binary variables. To speed up solving, this study uses variable substitution, which changes NFZ constraints into simpler upper and lower bounds and uses binary variables for path decision, matching the number of NFZs. Inspired by sequential convex programming (SCP), the issue with a mix of strategies that make the problem easier to solve is tackled, leading to the creation of the hybrid iterative convex programming (HICP) algorithm. Testing in different situations verify the effectiveness of the HICP algorithm. The HICP algorithm demonstrates robust adaptability and significant computational efficiency, capable of producing trajectories that navigate around the general NFZ in under 5 seconds, underscoring its substantial practical value. © 2024 SPIE.

关键词： mixed-integer linear programming

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Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference

Cycle-Configuration: A Novel Graph-theoretic Descriptor Set ...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Song, Bowen Zhu, Jianshen Azam, Naveed Ahmed Haraguchi, Kazuya Zhao, Liang Akutsu, Tatsuya Kyoto University Graduate School of Informatics Kyoto Japan Quaid-i-Azam University Department of Mathematics Islamabad Pakistan Kyoto University Graduate School of Advanced Integrated Studies in Human Survavibility Kyoto Japan Kyoto University Institute for Chemical Research Kyoto Japan

ISBN: (纸本)9798350386226

In this paper, we propose a novel family of descriptors of chemical graphs, named cycle-configuration (CC), that can be used in the standard "two-layered (2L) model"of mol-infer, a molecular inference framework based on mixed integer linear programming (MILP) and machine learning (ML). Proposed descriptors capture the notion of ortho/meta/para patterns that appear in aromatic rings, which has been impossible in the framework so far. Computational experiments show that, when the new descriptors are supplied, we can construct prediction functions of similar or better performance for all of the 27 tested chemical properties. We also provide an MILP formulation that asks for a chemical graph with desired properties under the 2L model with CC descriptors (2L+CC model). We show that a chemical graph with up to 50 non-hydrogen vertices can be inferred in a practical time. © 2024 IEEE.

关键词： mixed-integer linear programming

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Research on green multimodal transportation path optimization based on distributionally robust chance constraints with uncertain demand 8

Research on green multimodal transportation path optimizatio...

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8th International Conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Peng, Yu Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

Multimodal container transportation is an effective method for reducing carbon footprint levels. Considering the scenario of uncertain customer demand as well as carbon emission restrictions and cargo damage rate constraints, based on the probability distribution of uncertain demand mean and variance, this paper proposes the worst-case target chance constraint with customer demand being satisfied, establishes a green multimodal transportation route optimization model based on distributionally robust chance constraints with uncertain customer demand, and uses the DR-VaR concept to transform the model into a mixed integer linear programming model, which is then solved precisely. Through sensitivity analysis of the chance-constraint probability level of customer demand, it has been found that changes in the chance-constraint probability level lead to non-linear changes in the total cost. The higher the requirement of the chance-constraint probability level, the more it will increase the total cost of intermodal transportation. Compared to changing the transportation plan, the transportation network generally chooses to increase transportation costs to counter the disturbances of uncertain customer demands, and as much as possible, adhere to the original transportation routes and modes. © 2024 SPIE.

关键词： mixed-integer linear programming

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Research on motion planning of 6-DOF manipulator based on optimization algorithm 4

Research on motion planning of 6-DOF manipulator based on op...

引用

4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhang, Saiyang Huang, Junwei Li, Yaoyu Zhou, Yunzhao Feng, Yueqi Hebei Finance University Baoding China Southwest University of Science and Technology Mianyang China Wuhan Institute of Technology Wuhan China Hohai University Nanjing China

ISBN: (纸本)9798331541729

This study focuses on the motion planning of a 6-DOF robot arm in complex task scenarios, and deeply explores the key issues such as kinematic modeling, joint Angle path optimization, energy consumption minimization, and obstacle avoidance. By constructing the inverse kinematics model, using particle swarm optimization algorithm, combining mixed integer linear programming and A * algorithm, multi-objective genetic algorithm and hierarchical optimization strategy, the efficient motion planning of robot arm under different task requirements is realized. The research results show that the proposed model and algorithm significantly improve the performance and energy efficiency of the robot arm, which provides strong support for its wide application in industrial automation and other fields. © 2024 IEEE.

关键词： mixed-integer linear programming

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Waste-Free Energy Management in an Islanded Microgrid with Hydrogen Storage 2nd

Waste-Free Energy Management in an Islanded Microgrid with...

引用

2nd International Conference on Green Energy Conversion System, ICGECS 2023

作者： Gauché, Adrien Chenouard, Raphaël Morin, David Ghanes, Malek Nantes Université École Centrale Nantes CNRS LS2N UMR 6004 Nantes44000 France S.A. PowiDian La Ville-aux-Dames37700 France

ISBN: (纸本)9789819761470

This paper addresses the challenges associated with modeling storage systems and characteristics of hydrogen in the planning of island microgrids powered solely by renewable generation. We propose a novel mixed-integer linear programming (MILP) formulation that accounts for the non-constant efficiency of hydrogen chain storage, aiming to minimize error between Energy Management System (EMS) control and grid performance. Furthermore, our formulation incorporates flexible constraints to effectively manage surplus energy and mitigate energy wastage in scenarios with limited available resources. Through this research, we contribute to the development of optimized planning strategies for island microgrids, facilitating the integration of renewable energy sources and enhancing their operational efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： mixed-integer linear programming

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Optimal Flow User Connectivity Through Switches to Destiny Nodes in a Software-Defined Network 26

Optimal Flow User Connectivity Through Switches to Destiny N...

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2024 IEEE International Conference on Automation/26th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2024

作者： Viveros, Andŕes Adasme, Pablo Cordero, Sergio Juan, Enrique San Universidad de Santiago de Chile Chile Department of Electrical Engineering Santiago Chile

ISBN: (纸本)9798350378115

This article examines the management of user traffic to the network access point and within the network, from the user's access point to the destination server containing the required information. This study is conducted within the architecture of Software-Defined Networks (SDN). Two models are analyzed. The first model aims to minimize latency from the user to the network access point and within the network to retrieve information from the server while also minimizing the number of controllers. This model has a quadratic relationship between switches and controllers, which could affect performance. Subsequently, the second model linearizes the quadratic relationship. The goal is to manage the network by minimizing user access latency, considering that users will select the connection point with the minimum distance from the network access points to the destination server. For our research, we used 13 real Benchmark networks subject to extensive tests using our models and the computational power of the Gurobi software to find solutions. This work represents a pioneering effort in mathematical optimization related to the problem of how to manage the flow of user data from the point where the user connects to the network. The study focuses on doing this within a network that uses SDN technology. Detailed analysis of the test results shows that the models' effectiveness is closely linked to the network's scale. Additionally, compared to the first, the second model's performance generally performs worse in larger networks. These insights offer a deep understanding of the models' functionalities and optimization dynamics. They are finally, enhancing network efficiency and performance in SDNs. © 2024 IEEE.

关键词： mixed-integer linear programming

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Learning-Integrated Unit Commitment Optimization Based on GCN-GRU and Load Scenario Clustering 6

Learning-Integrated Unit Commitment Optimization Based on GC...

引用

6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Du, Sijun Wan, Jingyu Liu, Yifeng Qi, Xin Meng, Fei Shao, Peng Liu, Jun Ding, Tao Xi'an Jiaotong University School of Electrical Engineering Shaanxi Xi'an China State Grid Ningxia Electric Power Co. Ltd Ningxia Yinchuan China China Electric Power Research Institute Jiangsu Nanjing China

ISBN: (纸本)9798350377798

Unit commitment (UC) is a fundamental problem in power systems, typically formulated as a mixed-integer linear programming (MILP) model. As the scale of the system expands, numerous variables and constraints, especially binary variables, impose a significant computational burden on solving the UC problem. This paper proposes a learning-integrated optimization framework to accelerate the solution of the UC problem. First, a graph convolutional network (GCN)-gated recurrent unit (GRU) based model is employed to predict commitment decisions. Next, multiple prediction models are established by clustering load scenarios to reduce learning complexity. Finally, the UC problem is solved by fixing high-confidence predictions through confidence filtering. Case studies conducted on the IEEE 118-bus system using data generated from real-world load scenario characteristics demonstrate that the proposed framework effectively accelerates the solution of the UC problem with only a slight loss in solution quality. © 2024 IEEE.

关键词： mixed-integer linear programming

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Predicting Lagrangian Multipliers for mixed integer linear Programs 41

Predicting Lagrangian Multipliers for Mixed Integer Linear P...

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41st International Conference on Machine Learning, ICML 2024

作者： Demelas, Francesco Le Roux, Joseph Lacroix, Mathieu Parmentier, Axel Laboratoire d'Informatique de Paris-Nord Université Sorbonne Paris Nord - CNRS France CERMICS École des Ponts France

Lagrangian Relaxation stands among the most efficient approaches for solving mixed integer linear Programs (MILPs) with difficult constraints. Given any duals for these constraints, called Lagrangian Multipliers (LMs), it returns a bound on the optimal value of the MILP, and Lagrangian methods seek the LMs giving the best such bound. But these methods generally rely on iterative algorithms resembling gradient descent to maximize the concave piecewise linear dual function: the computational burden grows quickly with the number of relaxed constraints. We introduce a deep learning approach that bypasses the descent, effectively amortizing per instance optimization. A probabilistic encoder based on a graph neural network computes, given a MILP instance and its Continuous Relaxation (CR) solution, high-dimensional representations of relaxed constraints, which are turned into LMs by a decoder. We train the encoder and the decoder jointly by directly optimizing the bound obtained from the predicted multipliers. Our method is applicable to any problem with a compact MILP formulation, and to any Lagrangian Relaxation providing a tighter bound than CR. Experiments on two widely known problems, Multi-Commodity Network Design and Generalized Assignment, show that our approach closes up to 85 % of the gap between the continuous relaxation and the best Lagrangian bound, and provides a high-quality warm-start for descent-based Lagrangian methods. Copyright 2024 by the author(s)

关键词： mixed-integer linear programming

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HYBRID LOAD RESTORATION FOR HIERARCHICALLY CONTROLLED MICROGRIDS

HYBRID LOAD RESTORATION FOR HIERARCHICALLY CONTROLLED MICROG...

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CIRED Chicago Workshop 2024: Resilience of Electric Distribution Systems

作者： Feng, Fei Wang, Lizhi Zhou, Ella Zarrabian, Sina SUNY Maritime College United States Siemens United States

ISBN: (纸本)9781837242689

The increasing penetration of distributed energy resources (DERs) results in fast and large dynamic responses in microgrids during load restoration service due to low inertia and complex controls. To address this challenge, this paper devises a hybrid load restoration algorithm for hierarchically controlled microgrids to provide a frequency/voltage dynamics-constrained restoration service. Our contributions include: 1) a hierarchical control-integrated load restoration model for microgrids, considering power sharing and voltage regulation effects;2) dynamics-coupling constraints explicitly integrated the impacts of frequency and voltage dynamic responses into the restoration optimization model;3) a hybrid microgrid load restoration framework which integrates a mixed integer linear programming (MILP)-based optimization model and a dynamic simulation. Numerical simulations carried out on a typical 33-bus microgrid reveal the effectiveness of the hierarchically controlled microgrid load restoration under different scenarios. © The Institution of Engineering & Technology 2024.

关键词： mixed-integer linear programming

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