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Ana Carolina Olivera
Researcher at National University of Cuyo
Publications - 39
Citations - 414
Ana Carolina Olivera is an academic researcher from National University of Cuyo. The author has contributed to research in topics: Evolutionary algorithm & Job shop scheduling. The author has an hindex of 7, co-authored 33 publications receiving 314 citations. Previous affiliations of Ana Carolina Olivera include National Scientific and Technical Research Council & Universidad Nacional del Sur.
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Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization
TL;DR: This paper proposes an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs and achieves quantitative improvements for the two main objectives: the number of vehicles that reach their destination and the overall journey time.
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A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem
TL;DR: A Memetic Algorithm, based on the NSGAII (Non-Dominated Sorting Genetic Algorithm II) acting on two chromosomes, is introduced, which adds, to the genetic stage, a local search procedure (Simulated Annealing).
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Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization
TL;DR: A Swarm Intelligence approach is proposed for the optimal scheduling of traffic lights timing programs in metropolitan areas so that the traffic flow of vehicles can be improved with the final goal global target of reducing their fuel consumption and gas emissions.
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Multiobjective evolutionary optimization of traffic flow and pollution in Montevideo, Uruguay
TL;DR: A specific methodology combining simulation and multiobjective evolutionary methods to simultaneously optimize traffic flow and vehicular emissions via traffic lights planning in urban areas in Montevideo is proposed.
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Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm
Sergio Nesmachnow,Renzo Massobrio,Efraín Arreche,Christine L. Mumford,Ana Carolina Olivera,Pablo Vidal,Andrei Tchernykh +6 more
TL;DR: A parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles is introduced and achieves better quality of service when compared with the current reality.