A Population Learning Algorithm for Solving the Generalized Segregated Storage Problem

  • Dariusz Barbucha
  • Piotr Jedrzejowicz
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 5)


The paper presents a new population-based method, called population learning algorithm (PLA) for solving the Generalized Segregated Storage Problem (GSSP). PLA is an extension of population-based methods and adaptive memory programming techniques. It has been inspired by analogies to a social phenomenon rather than to a natural process. The paper introduces the GSSP, describes the concept of PLA and presents the application of PLA for solving GSSP. Computational experiment results are discussed in the final part of the paper.


Computational Experiment Operational Research Society Discrete Uniform Distribution Adaptive Memory Storage Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barbucha, D., Filipowicz, W. (1997): Segregated Storage Problems in Maritime Transportation. In: Papageorgiou, M., Pouliezos, A. (Eds.): Proceedings of the 8th IFAC Symposium in Transportation Systems’97, Chania, Vol. II, 569 - 573Google Scholar
  2. 2.
    Barbucha, D. (1999): Evolution-based algorithm to solve the Generalized Segregated Storage Problem. In: Adamski, A., Rudnicki, A., Zak, J., (Eds.): Modeling and Management in Transportation, Poznan-Krakow, Vol. 1, 159 - 164Google Scholar
  3. 3.
    Barbucha, D. (1999): A few effective heuristics to solve the Generalized Segregated Storage Problem. EURO PRIME I Conference, lSt Meeting of Young Europeans on Operational Research, WarsawGoogle Scholar
  4. 4.
    Evans, J.R., Tsubakitani, S. (1993): Solving the Segregated Storage Problem with Benders’ Partitioning. Journal of the Operational Research Society Vol. 44, No. 2, 175 - 184MATHGoogle Scholar
  5. 5.
    Jedrzejowicz, P. (1998): Social Learning Algorithm. Research Report 7/KI/98, Chair of Computer Science, Gdynia Maritime Academy, GdyniaGoogle Scholar
  6. 6.
    Jedrzejowicz, P. (1999): Social Learning Algorithm as a Tool for Solving Some Difficult Scheduling Problems. Foundation of Computing and Decision Sciences, Vol. 24, No 2, 51 - 66MathSciNetMATHGoogle Scholar
  7. 7.
    Neebe, A.W. (1987): An Improved, Multiplier Adjustment Procedure for the Segregated Storage Problem. Journal of the Operational Research Society, Vol. 38, No. 9, 815 - 825MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Dariusz Barbucha
    • 1
  • Piotr Jedrzejowicz
    • 1
  1. 1.Dept. of Computer ScienceGdynia Maritime AcademyGdyniaPoland

Personalised recommendations