Enhancing Accuracy of Hybrid Packing Systems through General-Purpose Characterization

  • Laura Cruz-Reyes
  • Claudia Gómez-Santillán
  • Satu Elisa Schaeffer
  • Marcela Quiroz-Castellanos
  • Victor M. Alvarez-Hernández
  • Verónica Pérez-Rosas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)


Some Hybrid Packing Systems integrate several algorithms to solve the bin packing problem (BPP) based on their past performance and the problem characterization. These systems relate BPP characteristics with the performance of the set of solution algorithms and allow us to estimate which algorithm is to yield the best performance for a previously unseen instance. The present paper focuses on the characterization of NP-hard problems. In related work, characterization metrics are traditionally oriented towards problem structure. In this work, we propose metrics based on descriptive statistics for the Bin Packing Problem (BPP). The proposed metrics are of general purpose, meaning that the metrics do not depend on problem structure and can be applied to BPP and other problems to complement existent metrics. The “enhanced” Hybrid Packing System outperforms the version that does not take advantage of the general-purpose metrics; the results obtained show a 3%-improvement with respect to the reference Packing System.


Hybrid Solution Systems Bin Packing Problem Problem Characterization Heuristic Algorithms Algorithm Selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Laura Cruz-Reyes
    • 1
  • Claudia Gómez-Santillán
    • 1
  • Satu Elisa Schaeffer
    • 2
  • Marcela Quiroz-Castellanos
    • 1
  • Victor M. Alvarez-Hernández
    • 1
  • Verónica Pérez-Rosas
    • 1
  1. 1.Instituto Tecnológico de Ciudad MaderoITCMMexico
  2. 2.Facultad de Ingeniería Mecánica y EléctricaUANLMexico

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