This article addresses the use of Holland's Genetic Algorithms (GAs) (Holland in Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 1975) in solving an optimization problem not exploited yet by literature, which we have named Optimal Billing Sequencing (OBS). The objective of the GA proposed is to automate pick sequencing, which addresses the process of allocating the stock available for sale to the purchase orders in a portfolio, so that the maximization of the billing is the optimal result for the OBS. A modelling and computational simulation methodology has been employed. Such methodology is designed to enable the GA to meet the boundary conditions established by predefined decision restrictions and parameters. We have reached the conclusion, by means of experimental tests, that the GA developed satisfactorily solves the problem studied. In addition to a low computational overhead, the GA reduces operating costs and speeds picking decision-making processes and billing processes.

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The search for more realistic modeling of financial time series reveals several stylized facts of real markets. In this work we focus on the multifractal properties found in price and index signals. Although the usual Minority Game (MG) models do not exhibit multifractality, we study here one of its variants that does. We show that the nonsynchronous MG models in the nonergodic phase is multifractal and in this sense, together with other stylized facts, constitute a better modeling tool. Using the Structure Function (SF) approach we detected the stationary and the scaling range of the time series generated by the MG model and, from the linear (nonlinear) behavior of the SF we identified the fractal (multifractal) regimes. Finally, using the Wavelet Transform Modulus Maxima (WTMM) technique we obtained its multifractal spectrum width for different dynamical regimes.

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