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Welcome to the ORCS Lab
Welcome to the Laboratory of Operational Research and Complex Systems. Feel free to explore the website and get to know our projects and the people behind the algorithms.
ORCS Lab - Optimisation, Data Science and Complex Systems
Research in the ORCS Lab focuses mainly on the development and application of optimisation methods to complex systems; on data science and statistical modeling; and on the applications of computational intelligence to large-scale power and energy systems.

Latest News

It's been a while since we posted updates here, so today we have a two-hit combo:

  • The PhD work of Alexandre Pimenta has just been accepted for publication at Springer's Computational Economics (IF: 0.691), under the title An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming. The work is authored by Alexandre Pimenta, Ciniro Nametala, Frederico G. Guimarães, and Eduardo Carrano;

  • George Fonseca is at it again, having just landed a paper in the European Journal of Operational Research (IF: 2.679). His work Integer Programming Techniques for Educational Timetabling was authored by himself, Haroldo Santos, Eduardo Carrano and Thomas Stidsen, and is one of the outcomes of George's time working under Prof. Stidsen at the Technical University of Denmark.

Click below to access both abstracts.

  • Accepted for the European Journal of Operational Research:

    • Title: Integer Programming Techniques for Educational Timetabling
    • Abstract: Educational timetabling problems require the assignment of times and resources to events, while sets of required and desirable constraints must be considered. The XHSTT format was adopted in this work because it models the main features of educational timetabling and it is the most used format in recent studies in the field. This work presents new cuts and reformulations for the existing integer programming model for XHSTT. The proposed cuts improved hugely the linear relaxation of the formulation, leading to an average gap reduction of 32\%. Applied to XHSTT-2014 instance set, the alternative formulation provided four new best known lower bounds and, used in a matheuristic framework, improved eleven best known solutions. The computational experiments also show that the resulting integer programming models from the proposed formulation are more effectively solved for most of the instances.
  • Accepted for Computational Economics:

    • Title: An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming
    • Abstract: Stock market automated investing is an area of strong interest for the academia, casual, and professional investors. In addition to conventional market methods, various sophisticated techniques have been employed to deal with such a problem, such as ARCH/GARCH predictors, artificial neural networks, fuzzy logic, etc. A computational system that combines a conventional market method (technical analysis -- TA), genetic programming, and multiobjective optimization is proposed in this work. This system was tested in six historical time series of representative assets from Brazil stock exchange market (BOVESPA). The proposed method leaded to profits considerably higher than the variation of the assets in the period. The financial return was positive even in situations in which the share lost market value.