Dpto. Estadística e Investigación Operativa - Estatistika eta Ikerketa Operatiboa Saila

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Browsing Dpto. Estadística e Investigación Operativa - Estatistika eta Ikerketa Operatiboa Saila by Author "Agustín Martín, Alba"

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    PublicationOpen Access
    Optimizing airline crew scheduling using biased randomization: a case study
    (Springer, 2016) Agustín Martín, Alba; Gruler, Aljoscha; Armas, Jesica de; Juan, Ángel A.; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    Various complex decision making problems are related to airline planning. In the competitive airline industry, ecient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of ight pairings allowing for cost minimizing crew- ight assignments. Experiments are done using a real-life case with dierent constraints. The results show that our easy-to-use and fast algorithm reduces overall crew ying times and the necessary number of accompanying crews compared to the pairings currently applied by the company.
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    PublicationOpen Access
    Using biased randomization for trajectory optimization in robotic manipulators
    (Springer, 2016) Agustín Martín, Alba; Olivares, Alberto; Staffetti, Ernesto; Estadística e Investigación Operativa; Estatistika eta Ikerketa Operatiboa
    We study the problem of optimization of trajectories for a robotic manipulator, with two degrees of freedom, which is constrained to pass through a set of waypoints in the workspace. The aim is to determine the optimal sequence of points and continuous optimal system trajectory. The actual formulation involves an optimal control problem of a dynamic system within integer variables that model the waypoints constrains. The nature of this problem, highly nonlinear and combinatorial, makes it particularly difficult to solve. The proposed method combines a meta-heuristic algorithm to determine the promising sequence of discrete points with a collocation technique to optimize the continuous path of the system. This method does not guarantee the global optimum, but can solve instances of dozens of points in reasonable computation time.