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Approximating Throughput of Small Production Lines Using Genetic Programming

  • Konstantinos Boulas
  • Georgios Dounias
  • Chrissoleon Papadopoulos
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Genetic Programming (GP) has been used in a variety of fields to solve complicated problems. This paper shows that GP can be applied in the domain of serial production systems for acquiring useful measurements and line characteristics such as throughput. Extensive experimentation has been performed in order to set up the genetic programming implementation and to deal with problems like code bloat or over fitting. We improve previous work on estimation of throughput for three stages and present a formula for the estimation of throughput of production lines with four stations. Further work is needed, but so far, results are encouraging.

Keywords

Production lines Genetic programming Symbolic regression Throughput 

Notes

Acknowledgement

This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program: Thales (ASPASIA). Investing in knowledge society through the European Social Fund.

References

  1. Angeline PJ, Kinnear KE (1996) Advances in genetic programming, vol 2. MIT Press, CambridgeGoogle Scholar
  2. Blumenfeld DE (1990) A simple formula for estimating throughput of serial production lines with variable processing times and limited buffer capacity. Int J Prod Res 28(6):1163–1182CrossRefGoogle Scholar
  3. Boulas K, Dounias G, Papadopoulos C, Tsakonas A (2015) Acquisition of accurate or approximate throughput formulas for serial production lines through genetic programming. Proceedings of 4th international symposium and 26th national conference on operational research, Hellenic Operational Research Society, pp 128–133Google Scholar
  4. Dallery Y, Frein Y (1993) On decomposition methods for tandem queueing networks with blocking. Oper Res 41(2):386–399CrossRefGoogle Scholar
  5. Dallery Y, Gershwin SB (1992) Manufacturing flow line systems: a review of models and analytical results. Queueing Syst 12(1–2):3–94CrossRefGoogle Scholar
  6. Diamantidis AC, Papadopoulos CT, Heavey C (2007) Approximate analysis of serial flow lines with multiple parallel-machine stations. IIE Trans 39(4):361–375CrossRefGoogle Scholar
  7. Heavy C, Papadopoulos H, Browne J (1993) The throughput rate of multistation unreliable production lines. Eur J Oper Res 68(1):69–89CrossRefGoogle Scholar
  8. Hunt GC (1956) Sequential arrays of waiting lines. Oper Res 4(6):674–683CrossRefGoogle Scholar
  9. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, CambridgeGoogle Scholar
  10. Koza JR (1994) Genetic programming II- automatic discovery of reusable programs. MIT Press, CambridgeGoogle Scholar
  11. Lai T (2003) Discovery of understandable math formulas using genetic programming. In: Genetic algorithms and genetic programming at Stanford. Stanford Bookstore, Stanford, pp 118–127Google Scholar
  12. Langdon WB (2000) Quadratic bloat in genetic programming. In: Whitley DL (ed) GECCO. Morgan Kaufmann Publishers, San Francisco, pp 451–458Google Scholar
  13. Li L, Qian Y, Du K, Yang Y (2015) Analysis of approximately balanced production lines. Int J Prod Res [online]: 1–18. doi: 10.1080/00207543.2015.1015750. Accessed 26 Apr 2015Google Scholar
  14. Lim J-T, Meerkov S, Top F (1990) Homogeneous, asymptotically reliable serial production lines: theory and a case study. IEEE Trans Autom Control 35(5):524–534CrossRefGoogle Scholar
  15. Martin G (1993) Predictive formulae for un-paced line efficiency. Int J Prod Res 31(8):1981–1990CrossRefGoogle Scholar
  16. Muth EJ (1984) Stochastic processes and their network representations associated with a production line queuing model. Eur J Oper Res 15(1):63–83CrossRefGoogle Scholar
  17. Muth EJ (1987) An update on analytical models of serial transfer lines. Department of Industrial and Systems Engineering, University of Florida, GainesvilleGoogle Scholar
  18. Papadopoulos C, Tsakonas A, Dounias G (2002) Combined use of genetic programming and decomposition techniques for the induction of generalized approximate throughput formulas in short exponential production lines with buffers. Proceedings of the 30th international conference on computers and industrial engineering, Tinos Island, Greece, vol II, pp 695–700Google Scholar
  19. Papadopoulos CT, O’Kelly ME, Vidalis MJ, Spinellis D (2009) Analysis and design of discrete part production lines, vol 31. Springer, New YorkGoogle Scholar
  20. Poli R, Langdon WB (1998) On the search properties of different crossover operators in genetic programming. In: Koza JR et al (eds) Genetic programming 1998: proceedings of the 3rd annual conference, Universtity of Wisconsin, pp 293–301Google Scholar
  21. Poli R, McPhee NF (2008) Covariant parsimony pressure in genetic programming. Technical report. CiteseerGoogle Scholar
  22. Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming (with contributions by Koza JR). In: A field guide to genetic programming. www.gp-field-guide.org.uk. Available via http://dces.essex.ac.uk/staff/rpoli/gp-field-guide/A_Field_Guide_to_Genetic_Programming.pdf. Accessed 1 Nov 2014

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Konstantinos Boulas
    • 1
  • Georgios Dounias
    • 1
  • Chrissoleon Papadopoulos
    • 2
  1. 1.Management and Decision Engineering Laboratory (MDE-Lab), Department of Financial and Management Engineering, Business SchoolUniversity of the AegeanChiosGreece
  2. 2.Department of EconomicsAristotle University of ThessalonikiThessalonikiGreece

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