Gabriel Fagúndez

Gabriel Fagúndez

Chicago, Illinois, United States
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About

As the Managing Director and Partner of Qubika, I oversee the operations and growth of a…

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Articles by Gabriel

  • Why do we read?
    Nov 5, 2020

    Why do we read?

    Originally published in my @Medium account. Spanish version here.

Activity

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Experience

  • Qubika Graphic

    Qubika

    Chicago, Illinois, United States

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    Austin, Texas, United States

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    Austin, Texas, Estados Unidos

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    Tacuarembó, Uruguay

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    Austin, Texas

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    Montevideo

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    Montevideo - Uruguay

Education

  • IEEM - Escuela de Negocios Graphic

    IEEM - Escuela de Negocios

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    Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information, whether such information is encoded as bits in a computer memory or transcribed in genes and protein structures in a biological cell. An alternate, more…

    Computer science is the scientific and practical approach to computation and its applications. It is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures (or algorithms) that underlie the acquisition, representation, processing, storage, communication of, and access to information, whether such information is encoded as bits in a computer memory or transcribed in genes and protein structures in a biological cell. An alternate, more succinct definition of computer science is the study of automating algorithmic processes that scale. A computer scientist specializes in the theory of computation and the design of computational systems.

Licenses & Certifications

Publications

  • Multiobjective taxi sharing optimization using the NSGA-II evolutionary algorithm

    MIC 2015

    This article presents the application of the NSGA-II multiobjective evolutionary algorithm to the problem of distributing passengers traveling from the same origin to different destinations in several taxis. A new problem formulation is presented, accounting for two quality of service metrics from the point of view of the users: minimize the total cost of the trips and the time of travel for each passenger from the origin to its destination. The proposed method follows a fully multiobjective…

    This article presents the application of the NSGA-II multiobjective evolutionary algorithm to the problem of distributing passengers traveling from the same origin to different destinations in several taxis. A new problem formulation is presented, accounting for two quality of service metrics from the point of view of the users: minimize the total cost of the trips and the time of travel for each passenger from the origin to its destination. The proposed method follows a fully multiobjective approach, and it is designed to provide an accurate and efficient way to solve realistic instances of the problem with high practical applicability. The experimental analysis compares the solutions found using the proposed algorithm versus those computed using a previous parallel micro evolutionary algorithm (following a linear aggregation approach to combine the problem objectives) and two greedy heuristics. The results show that the multiobjective evolutionary algorithm is able to efficiently reach significant improvements in both problem objectives in short execution times.

    See publication

  • Planificación multiobjetivo de viajes compartidos en taxis utilizando un micro algoritmo evolutivo paralelo

    MAEB 2015

    Este trabajo presenta la aplicación de un micro algoritmo evolutivo paralelo para resolver una variante multiobjetivo del problema de planificación de viajes compartidos en taxis. Los objetivos considerados en la variante abordada del problema son el costo total del viaje y la demora en llegar a destino por parte de cada pasajero. Esta versión del problema de viajes compartidos en taxis contempla situaciones realistas con alta aplicabilidad en la práctica, y toma en cuenta los criterios más…

    Este trabajo presenta la aplicación de un micro algoritmo evolutivo paralelo para resolver una variante multiobjetivo del problema de planificación de viajes compartidos en taxis. Los objetivos considerados en la variante abordada del problema son el costo total del viaje y la demora en llegar a destino por parte de cada pasajero. Esta versión del problema de viajes compartidos en taxis contempla situaciones realistas con alta aplicabilidad en la práctica, y toma en cuenta los criterios más considerados por los usuarios. El problema se resuelve con un micro algoritmo evolutivo paralelo que utiliza el modelo de subpoblaciones distribuidas y una estrategia de descomposición de dominio para contemplar las diferentes ponderaciones de los objetivos. El análisis experimental realizado sobre un conjunto de instancias reales del problema muestra que el algoritmo propuesto es ca- paz de encontrar significativas mejoras en ambos objetivos cuando se lo compara con estrategias ávidas intuitivas para resolver el problema.

    See publication

  • Online taxi sharing optimization using evolutionary algorithms

    IEEE

    This article presents the application of evolutionary algorithms for solving the problem of distributing a group of passengers travelling from the same origin to different destinations in several taxis, with the goal of minimizing the total cost of the trips. The experimental analysis compares the quality of the solutions found using the proposed algorithm versus those computed using an intuitive greedy heuristic similar to those previously proposed in the related literature to solve the…

    This article presents the application of evolutionary algorithms for solving the problem of distributing a group of passengers travelling from the same origin to different destinations in several taxis, with the goal of minimizing the total cost of the trips. The experimental analysis compares the quality of the solutions found using the proposed algorithm versus those computed using an intuitive greedy heuristic similar to those previously proposed in the related literature to solve the problem, showing that the evolutionary algorithm is able to reach significant improvements in the trips' total cost, outperforming the greedy heuristic in up to 41.7 % in the best case, and up to 36.4% on average. Two applications are also presented: a web-based user interface and a mobile application for devices using the iOS operating system.

    Other authors
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  • A parallel micro evolutionary algorithm for taxi sharing optimization

    ALIO 2014

    This article presents the application of a parallel micro evolutionary algorithm to the problem of distributing passengers traveling from the same origin to different destinations in several taxis, with the goal of minimizing the total cost of the trips. The proposed method is designed to provide an accurate and efficient way to solve the problem. The experimental analysis compares the solutions found using the proposed algorithm versus those computed using a sequential evolutionary algorithm…

    This article presents the application of a parallel micro evolutionary algorithm to the problem of distributing passengers traveling from the same origin to different destinations in several taxis, with the goal of minimizing the total cost of the trips. The proposed method is designed to provide an accurate and efficient way to solve the problem. The experimental analysis compares the solutions found using the proposed algorithm versus those computed using a sequential evolutionary algorithm and an intuitive greedy heuristic. The results show that the parallel evolutionary algorithm is able to efficiently reach significant im- provements in the total cost, outperforming the greedy heuristic in up to 36.1% (18.2% on average), and the sequential evolutionary algorithm in up to 8.5% (4.3% on average).

    See publication

Projects

  • The Malva Project

    Malva is a fork of the Mallba project, focusing on updating and improving the original project, and make it a collaborative open source project. It is an effort to develop, in an integrated way, a library of skeletons for combinatorial optimization (including exact, heuristic and hybrid methods) that can deal with parallelism in a user-friendly and, at the same time, efficient manner. Its three target environments are sequential computers, LANs of workstations and WANs.

    Main features…

    Malva is a fork of the Mallba project, focusing on updating and improving the original project, and make it a collaborative open source project. It is an effort to develop, in an integrated way, a library of skeletons for combinatorial optimization (including exact, heuristic and hybrid methods) that can deal with parallelism in a user-friendly and, at the same time, efficient manner. Its three target environments are sequential computers, LANs of workstations and WANs.

    Main features are:
    - Integration of all the skeletons under the same design principles.
    - Facility to switch from sequential to parallel optimization engines.
    - By providing sequential implementations users obtain parallel implementations.
    - Cooperation between engines makes possible to provide more powerful hybrid engines.
    - Ready to use on commodity machines.
    - Flexible and extensible software architecture. New skeletons can easily be added, alternative communication layers can be used, etc.

    See project

Languages

  • Inglés

    Full professional proficiency

  • Spanish

    Native or bilingual proficiency

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