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

共 2285 条记录，以下是271-280 订阅

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Robust Portfolio Optimization for Recommender Systems Considering Uncertainty of Estimated Statistics 21st

Robust Portfolio Optimization for Recommender Systems Consi...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Yanagi, Tomoya Ikeda, Shunnosuke Takano, Yuichi Graduate School of Science and Technology University of Tsukuba 1–1–1 Tennodai Ibaraki Tsukuba-shi305–8573 Japan Institute of Systems and Information Engineering University of Tsukuba 1–1–1 Tennodai Ibaraki Tsukuba-shi305–8573 Japan

ISBN: (纸本)9789819601240

This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings) required for mean–variance portfolio optimization are subject to inevitable estimation errors. To remedy this situation, we focus on robust optimization techniques that derive reliable solutions to uncertain optimization problems. Specifically, we propose a robust portfolio optimization model that copes with the uncertainty of estimated statistics based on the cardinality-based uncertainty sets. This robust portfolio optimization model can be reduced to a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. Experimental results using two publicly available rating datasets demonstrate that our method can improve not only the recommendation accuracy but also the diversity of recommendations compared with conventional mean–variance portfolio optimization models. Notably, our method has the potential to improve the recommendation quality of various rating prediction algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： mixed-integer linear programming

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Process Tree Alignments 28th

Process Tree Alignments

引用

28th International Conference on Enterprise Design, Operations, and Computing, EDOC 2024

作者： Schwanen, Christopher T. Pakusa, Wied van der Aalst, Wil M. P. RWTH Aachen University Aachen Germany Federal University of Applied Administrative Sciences Brühl Germany

ISBN: (纸本)9783031783371

The state-of-the-art approach for computing alignments is to apply an exhaustive state-space search with a well-tailored A*-heuristic function. If the heuristic fails to provide good estimates, the alignment computation quickly becomes infeasible even for small event logs given the exponential search space. Since intractability is unavoidable for general process models, we here consider the restricted class of process trees which provides a good balance between expressiveness and algorithmic feasibility. As our main result, we prove that alignments on process trees can be expressed as solutions of mixed integer linear Programs (MILP). Our novel approach does not only position the problem inside the class of NP, but also paves the way for applying a host of new optimization techniques from the field of mathematical programming to alignments on process trees. We further show that for process trees without parallel executions, our MILP formulation becomes a linear Program (LP) which can be solved efficiently. This result gives fresh insights into the structure of the alignment problem and the role of concurrency as a key factor for intractability. Finally, we implement our new algorithmic approach in PM4Py and evaluate the performance against the standard algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mixed-integer linear programming

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Optimal design of thermal storage in hybrid energy systems: Application to a laboratory-scale case study with experimental validation 41

Optimal design of thermal storage in hybrid energy systems: ...

引用

41st UIT International Heat Transfer Conference

作者： Bianco, N. Di Somma, M. Graditi, G. Mongibello, L. Dipartimento di Ingegneria Industriale Università Degli Studi di Napoli Federico II Napoli Italy ENEA- Agenzia Nazionale per le Nuove Tecnologie l'Energia e Lo Sviluppo Economico Sostenibile Roma Italy

The benefits of thermal storage in hybrid energy systems (HESs) are multiple, being among others the enhancement of flexibility of the network and of the efficiency in the energy resources use, by also reducing the overall cost of the energy system. Choosing the capacity of TSS to minimize the operational cost of the HES is not a trivial task, depending on several variables existing at both supply and demand sides. The contribution of this paper is to present the experimental validation of an innovative optimization model aiming to identify the optimal capacity of TSS in HESs that maximizes the economic performances. The optimization model is based on a mixed-integer linear programming approach and allows determining the optimal hourly operating strategies of the system components as well as the optimal capacity of the TSS, while minimizing the total daily energy costs. The optimized hourly operation schedule of the HES was simulated experimentally by using one of the systems from the TSS laboratory of ENEA Portici Research Center. The experimental results demonstrated the practical applicability of the proposed optimization model. In fact, a good overlap was found between the results obtained from the implementation of the optimization model with those obtained from the experimental simulation, both considering energy and economic data. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： mixed-integer linear programming

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Optimal Risk Scores for Continuous Predictors 10th

Optimal Risk Scores for Continuous Predictors

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10th International Conference on Machine Learning, Optimization, and Data Science, LOD 2024

作者： Molero-Río, Cristina D’Ambrosio, Claudia LIX CNRS École Polytechnique Institut Polytechnique de Paris Route de Saclay Palaiseau91128 France

ISBN: (纸本)9783031824807

In this paper, we propose a novel mixed-integer Non-linear Optimization formulation to construct a risk score, where we optimize the logistic loss with sparsity constraints. Previous approaches are typically designed to handle binary datasets, where continuous predictor variables are discretized in a preprocessing step by using arbitrary thresholds, such as quantiles. In contrast, we allow the model to decide for each continuous predictor variable the particular threshold that is critical for prediction. The usefulness of the resulting optimization problem is tested in synthetic datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mixed-integer linear programming

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BOOST: Microgrid Sizing Using Ordinal Optimization

BOOST: Microgrid Sizing Using Ordinal Optimization

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Chehade, Mohamad Fares El Hajj Karaki, Sami The University of Texas at Austin Chandra Department of Electrical and Computer Engineering AustinTX United States American University of Beirut Electrical and Computer Engineering Department Beirut Lebanon

ISBN: (纸本)9798331541125

The transition to sustainable energy systems has highlighted the critical need for efficient sizing of renewable energy resources in microgrids. In particular, designing photovoltaic (PV) and battery systems to meet residential loads is challenging due to trade-offs between cost, reliability, and environmental impact. While previous studies have employed dynamic programming and heuristic techniques for microgrid sizing, these approaches often fail to balance computational efficiency and accuracy. In this work, we propose BOOST, or Battery-solar Ordinal Optimization Sizing Technique, a novel framework for optimizing the sizing of PV and battery components in microgrids. Ordinal optimization enables computationally efficient evaluations of potential designs while preserving accuracy through robust ranking of solutions. To determine the optimal operation of the system at any given time, we introduce a mixed-integer linear programming (MILP) approach, which achieves lower costs than the commonly used dynamic programming methods. Our numerical experiments demonstrate that the proposed framework identifies optimal designs that achieve a levelized cost of energy (LCOE) as low as 8.84 ¢/kWh, underscoring its potential for cost-effective microgrid design. The implications of our work are significant: BOOST provides a scalable and accurate methodology for integrating renewable energy into residential microgrids, addressing economic and environmental goals simultaneously. © 2025 IEEE.

关键词： mixed-integer linear programming

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Efficient Online Path Selection and Workload Allocation for In-Network Computing in MEC 18th

Efficient Online Path Selection and Workload Allocation for...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ouyang, Sheng Zhang, Fanlong Mai, Junyu Chai, Yuan Chen, Quan Tao, Yongchao School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen Academy of Aerospace Technology Shenzhen China

ISBN: (纸本)9783031714696

In this paper, we study the joint optimization problem of computation path selection and workload allocation for in-network computing at the edge. The existing works, which only consider the end-to-end latency, ignore the operational cost of the servers and the dynamic tasks arrived in an online manner. Thus, we investigate the first online scheduling problem of path selection and workload allocation for in-network computing. Such a problem is modeled as a mixed integer programming problem, which tries to jointly minimize the server operating cost and end-to-end latency. Then, a dynamic 3D-map is constructed to take both the operation cost and end-to-end latency into account. Based on the constructed 3D-map, a cooperative learning based algorithm with ant colony optimization is proposed. Finally, the extensive simulation demonstrates the proposed algorithm shows good robustness and outperforms the state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mixed-integer linear programming

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Optimal Scheduling of Electricity-Hydrogen Energy Storage Considering Time-Of-Use Hydrogen Prices 11th

Optimal Scheduling of Electricity-Hydrogen Energy Storage Co...

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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Shen, Qianfeng Zhang, Shaohua Chang, Yixin Wang, Yufan Bai, Yilin Lyu, Chenghao Integrated Innovation Department China EPPEI Smart Energy Co. LTD. Beijing China School of Future Technology Xi’an Jiaotong University Xi’an710049 China School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9789819788316

The integration of renewable energy sources has significantly advanced sustainability efforts, yet their intermittent nature poses challenges to grid stability. Electricity-Hydrogen (EH) storage systems have emerged as promising solutions to store surplus renewable energy. Existing studies often overlook the technical disparities between Hydrogen Energy Storage Systems (HESS) and Battery Energy Storage Systems (BESS), highlighting the critical importance of considering Time-of-Use (TOU) hydrogen market prices in optimizing EH storage, as TOU pricing schemes incentivize efficient storage resource utilization and grid operation optimization. This paper addresses the pressing need for optimal scheduling strategies for EH storage systems in the context of renewable energy integration. The mathematical model is formulated by conducting comprehensive analyses to assess the economic aspects of EH storage under TOU hydrogen pricing scenarios. Additionally, a solution methodology based on mixed-integer-linear programming (MILP) and rolling optimization is employed to solve the problem. Through the comparative analysis, the results suggest that considering TOU prices of hydrogen can enhance the profitability of hydrogen trading, thereby improving the economic viability of the integrated power system. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： mixed-integer linear programming

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Power Grid Dispatch Modeling for the Prospective Current Trends Scenario for the ERCOT Service Area of Texas

Power Grid Dispatch Modeling for the Prospective Current Tre...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Das, Jani Tutay, Gizem Toplu Gulen, Gurcan Young, Michael Bureau of Economic Geology University of Texas AustinTX United States University of Texas at Austin Department of Mechanical Eng TX United States

ISBN: (纸本)9798331541125

The management of power systems depends on economic dispatch (EC) and constrained unit commitment (UC). A way to schedule generation to meet electricity demand (reliability), with a time-varying cycle (hourly, daily, weekly) at a minimum cost is by short term optimization using mixed integer linear program (MILP). This determines the optimum resource generation pattern for a given time based on resource characteristics, network topology and demand levels. This study presents a generation characteristics and cost prediction model for 2039, using the AURORA grid modeling software, and environmental impacts stemming from generation. The model simulates hourly system operation by assessing and analyzing resource adequacy and the impact of resource changes on system operation over time, while considering transmission congestion. The resource stack is based on the 'current trends' scenario published by ERCOT and the stack is varied to reflect different generation mixes. The results show congestion increases with penetration of wind/solar higher than 'current trends'. Increases in battery storage and transmission capacities mitigate congestion. The environmental impacts show the generation wise contribution for the modeled year, pointing to where technology and material substitutions could mitigate impacts, wherever possible. © 2025 IEEE.

关键词： mixed-integer linear programming

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Economic Optimial Scheduling Strategy for Thermal Power Units Under Deep Peak Regulation Considering Wind Power Integration 9th

Economic Optimial Scheduling Strategy for Thermal Power Unit...

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9th Purple Mountain Form on Smart Grid Protection and Control, PMF 2024

作者： Yang, Yuge Bao, Yuqing NARI School of Electrical and Automation Engineering Nanjing Normal University Nanjing210046 China

ISBN: (纸本)9789819616701

To expedite the energy transformation of the power system, the involvement of thermal power units (TPUs) in deep peak regulation (DPR) has become an effective strategy for enhancing the utilization of renewable energy. However, the optimal scheduling strategy of TPUs participating in DPR is characterized by multivariate, complex, and nonlinear mixed-integer programming problem. To address the problems of existing solution methods frequently falling into the local optimal solution and ignore the nonlinear characteristics and lead to inaccurate approximation results, an optimal scheduling model based on the multi-condition non-uniform piecewise linearization method was proposed, and the multi-condition objective function was converted into the form of non-piecewise linear equations by introducing decision variables, and the model was converted into a mixed integer linear programming (MILP) problem by using the non-uniform piecewise linearization method based on the optimal power segmentation interval of the multi-condition DPR model. Ultimately, the proposed method’s reliability and precision are confirmed through the analysis of various examples. © State Grid Electric Power 2025.

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...

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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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