Algorithm for Optimization of Multi-spindle Drilling Machine Based on Evolution Method

  • Paweł HoserEmail author
  • Izabella AntoniukEmail author
  • Dariusz StrzęciwilkEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 889)


The multi-spindle drills are often used within the mass furniture production. In this case the main factor is the optimization of equipment configuration as well as the working schedule of the drill, what leads to saving time, energy and to significantly lower the manufacturing costs. The optimization problem is hard and complicated. For the equipment and working schedule optimization the specific algorithm has been suggested, incorporating a set of heuristic methods. Among those, for setting the best head equipment of the machine head, the evolution algorithm was used. The initial analysis of the algorithm duty allows to suppose, that the evolution methods may be successfully incorporated for such kind of problems.


Multi-spindle drill Evolution algorithms Evolution computing 


  1. 1.
    Hoser, P., Podziewski, P., Kurek, J., Kruk, M.: Equipment optimization problem for multi-spindle computer controlled drilling machine. In: Computing in Science and Technology, pp. 91–108. Wydawnictwo Uniwersytetu Rzeszowskiego, Rzeszów (2017)Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search. Optimization, and Machine Learning. Addison-Veslay Publishing Company, Inc., Boston (1989)zbMATHGoogle Scholar
  3. 3.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer, 2003, 2015Google Scholar
  4. 4.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Algorithm. Springer, London (2015)zbMATHGoogle Scholar
  5. 5.
    Michalewicz, Z.: Genetic Algorithm + Data Structure = Evolutionary Programs. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  6. 6.
    Soille, P.: Morphological Image Analysis. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  7. 7.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum, New York (1972)Google Scholar
  8. 8.
    Chvatal, V.A.: Greedy heuristic for the set-covering problem. Math. Oper. Res. 4(3), 233–235 (1979)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Graham, L.R., Knuth, D.E., Patashnik, O.: Concrete Mathematics. A Foundation for Computer Science, 2nd edn. (2017)Google Scholar
  10. 10.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. Massachusetts Institute of Technology (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Applied Informatics and MathematicsWarsaw University of Life SciencesWarsawPoland

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