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In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who shou ...
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 ...
This thesis deals with models and methods for large scale optimization problems; in particular, we focus on decision problems arising in the context of seaport container terminals for the efficient management of terminal operations. Large-scale optimizatio ...
The paper presents an optimization design of an in-wheel BLDC motor for a kick scooter. The optimization is performed using a genetic optimization tool combined with a FEM commercial software. The new contributions of the paper are: (i) introduction of thr ...
This paper presents a calibration framework for a precipitation-runoff model for flood prediction in a mesoscale Alpine basin with strongly anthropogenic discharges. The developed methodology addresses two classical hydrological calibration challenges: com ...
Are multi-core SoCs being held back by the lack of adequate system design and software development tools? Multi-cores supply the advantages of flexible software-defined architectures, but support for system optimization, integration and verification is lac ...
In most applications related to transportation, it is of major importance to be able to identify the global optimum of the associated optimization problem. The work we present in this paper is motivated by the optimization problems arising in the maximum l ...
Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the u ...
The process industries are characterized by a large number of continuously operating plants, for which optimal operation is of economic importance. However, optimal operation is particularly difficult to achieve when the process model used in the optimizat ...
Discrete optimization is a difficult task common to many different areas in modern research. This type of optimization refers to problems where solution elements can assume one of several discrete values. The most basic form of discrete optimization is bin ...