Examinando por Autor "Lama, J."

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    A firefly algorithm to solve the manufacturing cell design problem
    (Springer Verlag, 2016) Soto, R.; Crawford, B.; Lama, J.; Paredes, F.
    The Manufacturing Cell Design Problem (MCDP) consists in creating an optimal design of production plants, through the creation of cells grouping machines that process parts of a given product. The goal is to reduce costs and increase productivity by minimizing movements and exchange of material between these cells. In this paper, we present a Firefly Algorithm (FA) to tackle this problem. The FA is a recent bio-inspired metaheuristic based on the mating behavior of fireflies that employ its flashing capabilities to communicate with each other or attract potential prey.We incorporate efficient transfer and discretization methods in order to suitable handle the binary domains of the problem. Interesting experimental results are illustrated where several global optimums are reached for a set of 90 well-known MCDP instances. © Springer International Publishing Switzerland 2016.
  • Miniatura
    ÍtemAcceso Abierto
    Resolución del manufacturing cell design problem utilizando el algoritmo Luciérnaga [A firefly algorithm to solve the manufacturing cell design problem]
    (IEEE Computer Society, 2016) Soto, R.; Crawford, B.; Lama, J.; Almonacid, B.
    This research focuses on modeling and solving the Manufacturing Cell Design Problem (MCDP) through a Firefly Algorithm (FA). The MCDP consists in creating an optimal design of production plants, through the creation of cells that group machines and parts. The goal of the problem is to minimize movements and exchange of material between the cells. The FA is a metaheuristic based on the mating behavior or flash of fireflies, in order to communicate with each other or attract potential prey. Fireflies move through the search space by means of attraction that they feel toward other fireflies until the stop criteria established is complied. Finally, to test the efficiency of FA, the results obtained have been compared with previous research illustrating encouraging results. © 2016 AISTI.