Intelligent Optimization Methods for Industrial Storage Systems

  • Mirko Ficko
  • Simon Klancnik
  • Simon Brezovnik
  • Joze Balic
  • Miran Brezocnik
  • Tone Lerher


The presented chapter introduces intelligent methods, which can be used for designing and managing of modern warehouses. Because of the ever-increasing complexity of such systems, the traditional methods cannot assure optimal or near-optimal solutions in design and operation. Demands for high utilization, flexibility, and the capacity to work reliably, even in changeable environments, can be met by adding intelligence to artificial system. The most promising intelligent methods are evolutionary computation and swarm intelligence which are unique methods of non-deterministic solving and optimizing. They proved to be effective and robust for planning and management of real systems. Evolutionary computation and swarm intelligence are methods, which were obtained from the observation of nature. Nature has some of the best answers to the problem of design and management. Therefore, this chapter tries to present intelligent methods to wider audience, and especially to experts and students of warehousing design and management.


Genetic Algorithm Particle Swarm Optimization Fitness Function Evolutionary Computation Travel Salesman 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.


  1. Azadivar F, Wang J (2000) Facility layout optimization using simulation and genetic algorithms. Int J Prod Res 38(17):4369–4383CrossRefzbMATHGoogle Scholar
  2. Banks J, Carson J, Nelson BL, Nicol D (1984) Discrete-event system simulations. Georgia Institute of Technology, AtlantaGoogle Scholar
  3. Brezočnik M (2001) A genetic-based approach to simulation of self-organizing assembly. Robotics Comp Integrated Manuf 17(1–2):113–120CrossRefGoogle Scholar
  4. Brezočnik M, Balic J (1997) Comparison of genetic programming with genetic algorithm. Proceedings of 3rd international conference design to manufacture in modern industry. Portorož, SloveniaGoogle Scholar
  5. Čerić V (1993) Simulacijsko modeliranje. University of Zagreb, ZagrebGoogle Scholar
  6. Curkovic P, Jerbic B (2007) Honey-bees optimization algorithm applied to path planning problem. Int J Simul Model 6(3):154–164CrossRefGoogle Scholar
  7. Di Caro G, Dorigo M (1998) AntNet: distributed stigmergetic control for communications networks. J Artif Intell Res 9:317–365zbMATHGoogle Scholar
  8. Dorigo M, Gambardella LM (1997) Ant colonies for the traveling salesman problem. BioSystems 43:73–81CrossRefGoogle Scholar
  9. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern 6:29–41Google Scholar
  10. Drstvenšek I (1998) Model tehnološke baze obdelovalnih operacij v postopkih optimiranja rezalnih pogojev z uporabo genetskih algoritmov. University of Maribor, MariborGoogle Scholar
  11. Engelbrecht A (2005) Fundamentals of computational swarm intelligence. Wiley, New YorkGoogle Scholar
  12. Ficko M, Brezocnik M, Balic J (2004) Designing the layout of single- and multiplerows flexible manufacturing system by genetic algorithms. J Mat Process Technol 157–158:150–158CrossRefGoogle Scholar
  13. Ficko M, Brezovnik S, Klancnik S, Balic J, Brezocnik M, Pahole I (2010) Intelligent design of an unconstrained layout for a flexible manufacturing system. Neurocomputing 73(4–6):639–647CrossRefGoogle Scholar
  14. Gambardella LM, Taillard ED, Dorigo M (1999) Ant colonies for the quadratic assignment problem. J Opl Res Soc 50:167–176zbMATHGoogle Scholar
  15. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. W.H. Freeman, New YorkzbMATHGoogle Scholar
  16. Gen MC (1997) Genetic algorithms and engineering design. Wiley, New YorkGoogle Scholar
  17. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, ReadingzbMATHGoogle Scholar
  18. Goldberg D, Lingle R (1985) Alleles, loci and the traveling salesman problem. 1st Conference on genetic algorithms, pp. 154–159Google Scholar
  19. Hassan R, Weck C (2005) Comparison of particle swarm optimization and the genetic algorithm. 46th AIAA/ASME/ASCE/AHS/ASC structures: structural dynamics and materials conference. Austin, TexasGoogle Scholar
  20. Hassan R, Cohanim B, Weck O (2005) Comparison of particle swarm optimization and the genetic algorithm. 46th AIAA/ASME/ASCE/AHS/ASC structures: structural dynamics and materials conference. Austin, TexasGoogle Scholar
  21. Heragu S, Kusiak A (1988) Machine layout problem in flexible manufacturing systems. Oper Res 36(2):258–268CrossRefGoogle Scholar
  22. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  23. Jamshidi M (1996) Large scale, systems: modeling, control, and fuzzy logic. Prentice-Hall, New JerseyGoogle Scholar
  24. Jones JL, Flynn A, Seiger BA (1999) Mobile robots. Natick: A. K. Peters cop, MassachusettszbMATHGoogle Scholar
  25. Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press Cambridge, MassachusettszbMATHGoogle Scholar
  26. Lerher T (2005) Simulacijski model visokoregalnega skladiščnega sistema (Doctor thesis izd.). University of Maribor, MariborGoogle Scholar
  27. Mitchell TM (1997) Machine learning. The McGraw-Hill Companies, New YorkzbMATHGoogle Scholar
  28. Pesl I, Zumer V, Brest J (2006) Optimization by ant colonies. J Electrotech Rev 73(2–3):93–98Google Scholar
  29. Rechenberg I (1974) Evolutionsstrategie: optimierung technischer systeme nach Prinzipien der biologischen evolution. Frommann-Holzboog Verlag, StuttgartGoogle Scholar
  30. Rodd M, Verbruggen H, Krijgsman A (1992) Artificial intelligence in real-time control. Eng Appl Artif Intell 5(5):385–399CrossRefGoogle Scholar
  31. Singh N (1996) Systems approach to computer-integrated design and manufacturing. Wiley, New YorkGoogle Scholar
  32. Smart WD (2002) Making reinforcement learning on real robots. Dissertation. Department of Computer Science, Brown UniversityGoogle Scholar
  33. Spears WM, Jong KA, Bäck T, Fogel DB, Garis HD (1993) An overview of evolutionary computation. Proceedings of the European conference on machine learning, vol. 667, 442–459. Springer, LondonGoogle Scholar
  34. Tavakkoli-Moghaddain R, Shayan E (1998) Facilities layout design by genetic algorithms. Comp Ind Eng 35(3–4):527–530CrossRefGoogle Scholar
  35. Wiendahl EHP, Fu Z (1992) Computer-aided analysis and planning of set-up process. Ann CIRP 41(1):497–500CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Mirko Ficko
    • 1
  • Simon Klancnik
    • 1
  • Simon Brezovnik
    • 1
  • Joze Balic
    • 1
  • Miran Brezocnik
    • 1
  • Tone Lerher
    • 1
  1. 1.Faculty of Mechanical EngineeringUniversity of MariborMariborSlovenia

Personalised recommendations