Results 41 to 50 of about 1,501,275 (273)
Transmission network expansion planning considering contingencies and uncertain demand
Revista Facultad de Ingeniería Universidad de Antioquia, 2013 This paper presents a methodology and a mathematical model to solve the expansion planning problem that takes into account the effect of contingencies in the planning stage, and considers the demand as a stochastic variable within a specified range.
Luis Alfonso Gallego-Pareja +2 more
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Diverse Landscape of Tunable Magnetic, Topological, and Ferroelectric States in 2D Ti3Se3Te2
Advanced Science, EarlyView.Ti3Se3Te2 emerges as a multifunctional 2D van der Waals platform. The monolayer is a dynamically stable ferromagnetic quantum anomalous Hall insulator. In bilayers, two stacking configurations yield distinct phases: AA‐stacking hosts an altermagnetic quantum spin Hall insulator, while AA′‐stacking exhibits three‐state in‐plane ferroelectricity ...
Jiangtao Yu +5 more
wiley +1 more source
On the solution of stochastic multiobjective integer linear programming problems with a parametric study [PDF]
Journal of the ACS Advances in Computer Science, 2009 In this study we consider a multiobjective integer linear stochastic programming problem with individual chance constraints. We assume that there is randomness in the right-hand sides of the constraints only and that the random variables are normally ...
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A stochastic programming approach to perform hospital capacity assessments.
PLoS ONE, 2023 This article introduces a bespoke risk averse stochastic programming approach for performing a strategic level assessment of hospital capacity (QAHC). We include stochastic treatment durations and length of stay in the analysis for the first time. To the
Robert L Burdett +4 more
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Approximation in stochastic integer programming
, 2003 Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solutions.
Stougie, Leen, Vlerk, Maarten H. van der
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Cutting Planes for Multistage Stochastic Integer Programs [PDF]
Operations Research, 2009 This paper addresses the problem of finding cutting planes for multistage stochastic integer programs. We give a general method for generating cutting planes for multistage stochastic integer programs based on combining inequalities that are valid for the individual scenarios.
Yongpei Guan +2 more
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Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization
Advanced Science, EarlyView.A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley +1 more source
CSEE Journal of Power and Energy Systems, 2019 A novel stochastic optimal dispatch model is proposed considering medium and long-term electricity transactions for a wind power integrated energy system using chance constrained programming.
Gang Wu +4 more
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Decomposition of test sets in stochastic integer programming [PDF]
Mathematical Programming, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Raymond Hemmecke, Rüdiger Schultz
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Integration of Reconfigurable p‐Bit and 1R Crossbar Array for Memristive Probabilistic Computing
Advanced Science, EarlyView.A memristive probabilistic computing system is demonstrated by integrating stochastic p‐bits based on volatile memristors with a 1R crossbar array encoding interaction weights. The system performs weighted‐sum operations across the array and updates p‐bits iteratively.
Keunho Soh +7 more
wiley +1 more source