Combining Stochastic Optimization and Monte-Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment
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Call centre scheduling aims to determine the workforce so as to meet target service levels. The service level depends on the mean rate of arrival calls, which fluctuates during the day, and from day to day. The staff schedule must adjust the workforce peri ...
The paper studies the benefits of multi-path content delivery from a rate-distortion efficiency perspective. We develop an optimization framework for computing transmission schedules for streaming media packets over multiple network paths that maximize the ...
We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solutio ...
The aim of this project is to integrate uncertainty analysis in a thermo-economic optimization framework to be used as decision making support in the design of energy systems.Tree comprehensive thermo-economic models of fuel cells systems have been develop ...
We aim at the elaboration of Information Systems able to optimize energy consumption in buildings while preserving human comfort. Our focus is in the use of state-based stochastic modeling applied to temporal signals acquired from heterogeneous sources suc ...
EPFL Solar Energy and Building Physics Laboratory (LESO-PB)2013
Dynamic stochastic optimization problems with a large (possibly infinite) number of decision stages and high-dimensional state vectors are inherently difficult to solve. In fact, scenario tree-based algorithms are unsuitable for problems with many stages, ...
Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the use of s ...
We propose a new heuristic for nonlinear global optimization combining a variable neighborhood search framework with a modified trust-region algorithm as local search. The proposed method presents the capability to prematurely interrupt the local search if ...
The spectrum of exponentially weighted covariance matrices is studied here. We first give a formal way of obtaining the spectrum using annealed approximation frequently used in Statistical Mechanics. Using the method developed by Bai and Silverstein we obt ...
In this paper we focus on the application of global stochastic optimization methods to extremum estimators. We propose a general stochastic method the master method which includes several stochastic optimization algorithms as a particular case. The propose ...