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

共 2277 条记录，以下是501-510 订阅

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Electric Vehicle Routing Problem with Multiple Resource Constraints

Electric Vehicle Routing Problem with Multiple Resource Cons...

引用

2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： He, Zhiye Yang, Zaiyue Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9789531842976

This paper addresses the Electric Vehicle Routing Problem (EVRP) with multi-resource constraints. We propose a novel mathematical model incorporating multiple resource constraints into the EVRP. While the flexibility of this model is advantageous, it is important to acknowledge that the computational complexity of solving EVRP increases with problem size, as the problem is known to be NP-hard. To tackle this challenge, we propose a two-stage iterative algorithm. The algorithm transforms the original mixed non-linear integer programming problem into two tractable linear programming (LP) subproblems. The first stage focuses on optimizing vehicle routing, and the second stage tackles the resource scheduling problem, ensuring all resource limitations are met. Besides, an iterative optimization process is proposed to improve the performance of the results further. Through numerical experiments, we verify the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： mixed-integer linear programming

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A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids 13

A Fast Dynamic Internal Predictive Power Scheduling Approach...

引用

13th IEEE Power and Energy Society: Innovative Smart Grid Technologies - Asia, ISGT Asia 2024

作者： Maya, Neethu Poolla, Bala Kameshwar Srinivasan, Seshadhri Sundararajan, Narasimhan Sundaram, Suresh Indian Institute of Science Department of Aerospace Engineering Bangalore India National Renewable Energy Laboratory GoldenCO United States Tvs Sensing Solutions Madurai India Nanyang Technological University School of Electrical and Electronics Engineering Singapore

ISBN: (纸本)9798350373929

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILPPS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to specified time durations. © 2024 IEEE.

关键词： mixed-integer linear programming

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Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary 33

Tackling Stackelberg Network Interdiction against a Boundedl...

引用

33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Mai, Tien Bose, Avinandan Sinha, Arunesh Nguyen, Thanh Singh, Ayushman Kumar Singapore Management University Singapore University of Washington United States Rutgers University United States University of Oregon United States Indian Institute of Technology Delhi India

ISBN: (纸本)9781956792041

This work studies Stackelberg network interdiction games - an important class of games in which a defender first allocates (randomized) defense resources to a set of critical nodes on a graph while an adversary chooses its path to attack these nodes accordingly. We consider a boundedly rational adversary in which the adversary's response model is based on a dynamic form of classic logit-based (quantal response) discrete choice models. The resulting optimization is non-convex and additionally, involves complex terms that sum over exponentially many paths. We tackle these computational challenges by presenting new efficient algorithms with solution guarantees. First, we present a near optimal solution method based on path sampling, piece-wise linear approximation and mixed-integer linear programming (MILP) reformulation. Second, we explore a dynamic programming based method, addressing the exponentially-many-path challenge. We then show that the gradient of the non-convex objective can also be computed in polynomial time, which allows us to use a gradient-based method to solve the problem efficiently. Experiments based on instances of different sizes demonstrate the efficiency of our approaches in achieving near-optimal solutions. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： mixed-integer linear programming

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Collaborative Allocation of Flexibility Resources in Source-Load-Storage Systems for High New Energy Penetration

Collaborative Allocation of Flexibility Resources in Source-...

引用

2024 International Conference on New Power System and Power Electronics, NPSPE 2024

作者： Chen, Baosheng Zhang, Zelong Yang, Longyu Yang, Yan Liu, Zhijun State Grid Ningxia Electric Power Co. Ltd. Eco-Tech Research Institute Yinchuan China Sated Grid Shizuishan Power Supply Company Shizuishan China

ISBN: (纸本)9798350389869

In the context of the "dual carbon"goal, China is gradually constructing a new type of power system with high penetration of new energy grid-connected, in order to adapt to the characteristics of the power system with high penetration of new energy grid-connected, this paper proposes a method for planning and allocating resources based on the balance of supply and demand across source, load, and storage flexibility. It determines the capacity for each resource within the new power system, aiming to minimize total construction and operational costs. The study concludes that optimizing flexibility resource allocation is essential for balancing new energy integration, reducing power supply costs, and ensuring the stability and economic viability of power systems, paving the way for a sustainable, low-carbon energy future. © 2024 IEEE.

关键词： mixed-integer linear programming

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Sensing Resource Allocation Against Data-Poisoning Attacks in Traffic Routing 63

Sensing Resource Allocation Against Data-Poisoning Attacks i...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Yu, Yue Thorpe, Adam J. Milzman, Jesse Fridovich-Keil, David Topcu, Ufuk University of Minnesota Twin Cities Department of Aerospace Engineering and Mechanics MinneapolisMN United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences Department of Aerospace Engineering and Engineering Mechanics AustinTX United States Devcom Army Research Laboratory 2800 Powder Mill Rd AdelphiMD United States

ISBN: (纸本)9798350316339

Data-poisoning attacks can disrupt the efficient operations of transportation systems by misdirecting traffic flows via falsified data. One challenge in countering these attacks is to reduce the uncertainties on the types of attacks, such as the distribution of their targets and intensities. We introduce a resource allocation method in transportation networks to detect and distinguish different types of attacks and facilitate efficient traffic routing. The idea is to first cluster different types of attacks based on the corresponding optimal routing strategies, then allocate sensing resources to a subset of network links to distinguish attacks from different clusters via lexicographical mixed-integer programming. We illustrate the application of the proposed method using the Anaheim network, a benchmark model in traffic routing that contains more than 400 nodes and 900 links. © 2024 IEEE.

关键词： mixed-integer linear programming

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Towards Sustainable Edge Computing: Efficient Task Offloading for Energy Efficiency and Latency Reduction

Towards Sustainable Edge Computing: Efficient Task Offloadin...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Moshiri, Parisa Fard Simsek, Murat Kantarci, Burak University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

ISBN: (纸本)9798350351255

In networks with limited resources, the concept of offloading computation to Mobile Edge Computing (MEC) has emerged as a promising research direction with the advent of new services in fifth-generation (5G) networks. However, poorly designed offloading strategies can lead to excessive energy consumption and unpredictable latency, while the number of dropped tasks significantly impacts system efficiency. This paper presents a 5G-MEC task offloading scenario aimed at minimizing computation and communication latency, energy consumption, and the rate of dropped tasks. To achieve this, we employ mixed integer non-linear programming (MINLP) and mixed integer linear programming (MILP), comparing their performance with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Our analysis considers the impact of the quantity of tasks and User Equipment (UE) on network parameters, distinguishing between urgent and non-urgent tasks. We ensure a zero-dropped task rate for urgent tasks. The proposed approach outperforms baseline techniques such as First Come First Serve (FCFS), Shortest Deadline First (SDF), and Urgent Tasks First (UTF) in the context of 5G-MEC task offloading. Specifically, compared to MILP, PSO, and GA, the MINLP-based approach reduces total latency by 12%, 34%, and 44%, respectively. Moreover, it decreases energy consumption by 8%, 30%, and 47% compared to MILP, PSO, and GA, respectively. The dropped task ratio is also reduced by 17%, 42%, and 65% under the MINLP-based approach compared to MILP, PSO, and GA, respectively. © 2024 IEEE.

关键词： mixed-integer linear programming

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Multi-Objective Procedure for Optimal Integration of DC Microgrids in the Distribution Network 24

Multi-Objective Procedure for Optimal Integration of DC Micr...

引用

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： Marasciuolo, Francesca Dicorato, Maria Tricarico, Gioacchino Forte, Giuseppe Sbrizzai, Roberto Bari Italy

ISBN: (纸本)9798350355185

The combination of renewable energy sources, storage systems, and electric vehicle stations in a microgrid framework represents a useful solution for electric mobility integration in the distribution network. The coordination between control strategies of microgrids and distribution network is desirable for ensuring safe and cost-effective operation compliant with technical features of the involved infrastructures. In this study, a multi-objective mixed-integer linear programming procedure is proposed aiming at achieving both grid-side and microgrid-side technical optimal targets in the day-ahead scheduling of a radial distribution network integrating DC microgrids. The procedure is applied to a 33-nodes test network integrating 4 DC microgrids based on data of an experimental facility. © 2024 IEEE.

关键词： mixed-integer linear programming

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Reachability analysis for piecewise affine systems with neural network-based controllers 63

Reachability analysis for piecewise affine systems with neur...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Teichrib, Dieter Darup, Moritz Schulze Tu Dortmund University Control and Cyberphysical Systems Group Department of Mechanical Engineering Germany

ISBN: (纸本)9798350316339

Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN -based controllers that are fast to evaluate. However, when approximating control laws using NN, performance and stability guarantees of the original controller may not be preserved. Recently, it has been shown that it is possible to provide such guarantees for linear systems with NNbased controllers by analyzing the approximation error with respect to a stabilizing base-line controller or by computing reachable sets of the closed-loop system. The latter has the advantage of not requiring a base-line controller. In this paper, we show that similar ideas can be used to analyze the closed-loop behavior of piecewise affine (PWA) systems with an NNbased controller. Our approach builds on computing overapproximations of reachable sets using mixed-integer linear programming, which allows to certify that the closed-loop system converges to a small set containing the origin while satisfying input and state constraints. We also show how to modify a given NN-based controller to ensure asymptotic stability for the controlled PWA system. © 2024 IEEE.

关键词： mixed-integer linear programming

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Optimization Model for Coordinated Multistage Planning of the Generation-Transmission System with Demand Forecasting Using Neural Networks 4th

Optimization Model for Coordinated Multistage Planning of th...

引用

4th International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Quinatoa, Carlos Chasi, Alex Puetate, Vicky Casilimas, Alexander Camacho, Luis Ortiz, Josue Technical University of Cotopaxi Cotopaxi Latacunga050101 Ecuador National Autonomous University of Mexico Engineering Institute Mexico Mexico City01020 Mexico

ISBN: (纸本)9789819606436

The implementation of a multistate planning model for power generation-transmission expansion, as opposed to traditional independent planning frameworks, addresses the escalating energy demand and the need for efficient planning. This study applies a multistate DC-planning model to the 230 kV network of Ecuador’s National Interconnected System (NIS), aiming to minimize operational costs and the construction of new infrastructure. By employing a mixed-integer nonlinear programming (MINLP) approach and a neural network-generated demand forecasts, the electricity expansion spanning the period from 2023 to 2033 is analyzed. This methodology facilitates the delineation of expansion zones, thereby mitigating long-term costs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： mixed-integer linear programming

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Explicit Bounds for Large Gaps Between Cubefree integers 20th

Explicit Bounds for Large Gaps Between Cubefree Integers

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20th Combinatorial and Additive Number Theory, New York Number Theory Seminar, CANT 2022 and 21st Combinatorial and Additive Number Theory, New York Number Theory Seminar, CANT 2023

作者： Kumchev, Angel McCormick, Wade McNew, Nathan Park, Ariana Scherr, Russell Ziehr, Willow Department of Mathematics Towson University Towson MD21252 United States Department of Mathematics Northwestern University EvanstonIL60208 United States School of Mathematics University of Minnesota Twin Cities MinneapolisMN55455 United States Department of Mathematics University of California Berkeley BerkeleyCA94720 United States

ISBN: (纸本)9783031650635

We obtain explicit forms of the current best known asymptotic upper bounds for gaps between cubefree integers. In particular, we show that the interval (Formula presented) contains a cubefree integer for any (Formula presented). The constant 5 can be improved further, if. x is assumed to be larger than a very large constant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mixed-integer linear programming

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