Skip to main content

Automating the Parameter Selection in VRP: An Off-line Parameter Tuning Tool Comparison

  • Chapter
  • First Online:

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 34))

Abstract

Vehicle route optimization is an important application of combinatorial optimization. Therefore, a variety of methods has been proposed to solve different challenging vehicle routing problems. An important step in adopting these methods to solve real-life problems is to find appropriate parameters for the routing algorithms. In this chapter, we show how this task can be automated using parameter tuning by presenting a set of comparative experiments on seven state-of-the-art tuning methods. We analyze the suitability of these methods in configuring routing algorithms, and give the first critical comparison of automated parameter tuners in vehicle routing. Our experimental results show that the tuning methods are able to effectively automate the task of parameter configuration of route optimization systems. Moreover, our comparison shows that while routing algorithms clearly benefit from parameter tuning, and while there is no single tuner which consistently outperforms others, the tuning performance can be clearly improved with careful choice of a tuning method.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    was disabled for its tendency to produce infeasible route

  2. 2.

    http://users.jyu.fi/~juherask/tuning/.

  3. 3.

    Version 0.9.93.4r2658, http://www.lri.fr/~hansen/cmaesintro.html.

  4. 4.

    Version 0.9, http://iridia.ulb.ac.be/irace/.

  5. 5.

    Version 2.3.5, http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/

    Version 2.0.2, http://www.cs.ubc.ca/labs/beta/Projects/SMAC/.

References

  1. Ansótegui C, Sellmann M, Tierney K (2009) A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent IP (ed) CP’09 Proceedings of the 15th international conference on principles and practice of constraint programming. Lecture notes in computer science, vol 5732. Springer, Berlin, pp 142–157

    Google Scholar 

  2. Balaprakash P, Birattari M, Stützle T (2007) Improvement strategies for the F-Race algorithm: sampling design and iterative refinement. IRIDIA—technical report series TR/IRIDIA/2007-011, Université Libre de Bruxelles

    Google Scholar 

  3. Baldacci R, Bartolini E, Mingozzi A, Roberti R (2010) An exact solution framework for a broad class of vehicle routing problems. Comput Manag Sci 7(3):229–268

    Article  MATH  MathSciNet  Google Scholar 

  4. Bartz-Beielstein T, Lasarczyk C, Preuß M (2005) Sequential parameter optimization. In: The 2005 IEEE congress on evolutionary computation, vol 1. IEEE Press, pp 773–780

    Google Scholar 

  5. Battiti R, Brunato M (2010) Reactive search optimization: learning while optimizing. In: Gendreau M, Potvin JY (eds) Handbook of metaheuristics, 2nd edn. Springer, Berlin, pp 543–571

    Google Scholar 

  6. Becker S, Gottlieb J, Stützle T (2006) Applications of racing algorithms: an industrial perspective. In: EA’05 proceedings of the 7th international conference on artificial evolution. Lecture notes in computer science, vol 3871. Springer, Berlin, pp 271–283

    Google Scholar 

  7. Bianchi L, Birattari M, Chiarandini M, Manfrin M, Mastrolilli M, Paquete L, Rossi-Doria O, Schiavinotto T (2006) Hybrid metaheuristics for the vehicle routing problem with stochastic demands. J Math Model Algorithms 5(1):91–110

    Article  MATH  MathSciNet  Google Scholar 

  8. Birattari M, Stützle T, Paquete L, Varrentrapp K (2002) A racing algorithm for configuring metaheuristics. In: GECCO 2002 proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, CA, pp 11–18

    Google Scholar 

  9. Birattari M, Yuan Z, Balaprakash P, Stützle T (2010) F-Race and iterated F-Race: an overview. In: Bartz-Beielstein T, Chiarandini M, Paquete L, Preuss M (eds) Experimental methods for the analysis of optimization algorithms. Springer, Berlin, pp 311–336

    Google Scholar 

  10. Christofides N, Mingozzi A, Toth P (1979) The vehicle routing problem. In: Christofides N, Mingozzi A, Toth P, Sandi C (eds) Combinatorial optimization. Wiley, Chichester, pp 315–338

    Google Scholar 

  11. Coy SP, Golden BL, Runger GC, Wasil EA (2001) Using experimental design to find effective parameter settings for heuristics. J Heuristics 7(1):77–97

    Article  MATH  Google Scholar 

  12. Dantzig GB, Ramser JH (1959/1960) The truck dispatching problem. Manage Sci 6:80–91

    Article  MathSciNet  Google Scholar 

  13. Drexl M (2011) Rich vehicle routing in theory and practice. Technical report LM-2011-04, Johannes Gutenberg University, Mainz

    Google Scholar 

  14. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolut Comput 3(2):124–141

    Article  Google Scholar 

  15. Garrido P, Castro C, Monfroy E (2009) Towards a flexible and adaptable hyperheuristic approach for VRPs. In: Arabnia HR, de la Fuente D, Olivas JA (eds.) Proceedings of the 2009 international conference on artificial intelligence (ICAI 2009). CSREA Press, pp 311–317

    Google Scholar 

  16. Gendreau M, Laporte G, Séguin R (1996) Stochastic vehicle routing. Europ J Oper Res 88(1):3–12

    Article  MATH  Google Scholar 

  17. Groër C, Golden B, Wasil E (2010) A library of local search heuristics for the vehicle routing problem. Math Program Comput 2(2):79–101

    Article  MATH  MathSciNet  Google Scholar 

  18. Hansen N (2006) The CMA evolution strategy: a comparing review. In: Lozano JA, Larrañaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation: advances on estimation of distribution algorithms. Springer, Berlin, pp 75–102

    Google Scholar 

  19. Hepdogan S, Moraga R, DePuy G, Whitehouse G (2007) Nonparametric comparison of two dynamic parameter setting methods in a meta-heuristic approach. J Syst Cybern Inform 5(5):46–52

    Google Scholar 

  20. Hutter F, Hoos HH, Leyton-Brown K (2010) Automated configuration of mixed integer programming solvers. In: Lodi A, Milano M, Toth P (eds) Integration of AI and OR techniques in constraint programming for combinatorial optimization problems. Lecture notes in computer science, vol 6140. Springer, Berlin, pp 186–202

    Google Scholar 

  21. Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: Coello-Coello CA (ed) Learning and intelligent optimization: 5th international conference (LION 5, Rome, 2011). Lecture notes in computer science, vol 6683. Springer, Berlin, pp 507–523

    Google Scholar 

  22. Hutter F, Hoos HH, Leyton-Brown K, Stützle T (2009) ParamILS: an automatic algorithm configuration framework. J Artif Intell Res (JAIR) 36:267–306

    MATH  Google Scholar 

  23. Kadioglu S, Malitsky Y, Sellmann M, Tierney K (2010) ISAC— instance-specific algorithm configuration. In: Coelho H, Studer R, Wooldridge M (eds) ECAI 2010–19th European conference on artificial intelligence. IOS Press, Amsterdam, pp 751–756

    Google Scholar 

  24. Laporte G (2007) What you should know about the vehicle routing problem. Naval Res Logist 54(8):811–819

    Article  MATH  MathSciNet  Google Scholar 

  25. López-Ibáñez M, Dubois-Lacoste J, Stützle T, Birattari M (2011) The irace package: iterated racing for automatic algorithm configuration. IRIDIA—technical report series TR/IRIDIA/2011-004, Université Libre de Bruxelles

    Google Scholar 

  26. Miki M, Hiroyasu T, Jitta T (2003) Adaptive simulated annealing for maximum temperature. In: 2003 IEEE international conference on systems, man and cybernetics. IEEE, vol 1, pp 20–25

    Google Scholar 

  27. Montero E, Riff MC, Neveu B (2010) An evaluation of off-line calibration techniques for evolutionary algorithms. In: GECCO’10 proceedings of the 12th annual conference on genetic and evolutionary computation. ACM, New York, pp 299–300

    Google Scholar 

  28. Montero E, Riff MC, Neveu B (2010) New requirements for off-line parameter calibration algorithms. In: 2010 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8

    Google Scholar 

  29. Nannen V, Eiben AE (2007) Efficient relevance estimation and value calibration of evolutionary algorithm parameters. In: CEC 2007 IEEE congress on evolutionary computation. IEEE, pp 103–110

    Google Scholar 

  30. Pellegrini P (2005) Application of two nearest neighbor approaches to a rich vehicle routing problem. In: IRIDIA—technical report series TR/IRIDIA/2005-015, Université Libre de Bruxelles

    Google Scholar 

  31. Pellegrini P, Birattari M (2006) The relevance of tuning the parameters of metaheuristics. A case study: the vehicle routing problem with stochastic demand. IRIDIA—technical report series TR/IRIDIA/2006-008, Université Libre de Bruxelles

    Google Scholar 

  32. Smit SK, Eiben AE (2009) Comparing parameter tuning methods for evolutionary algorithms. In: CEC ’09 IEEE congress on evolutionary computation. IEEE, pp 399–406

    Google Scholar 

  33. Toth P, Vigo D (eds) (2002) The vehicle routing problem. SIAM, Philadelphia, PA

    MATH  Google Scholar 

  34. Vidal T, Crainic TG, Gendreau M, Lahrichi N, Rei W (2012) A hybrid genetic algorithm for multidepot and periodic vehicle routing problems. Oper Res 60(3):611–624

    Article  MATH  MathSciNet  Google Scholar 

  35. Yuan Z, Montes de Oca, MA, Birattari M, Stützle T (2010) Modern continuous optimization algorithms for tuning real and integer algorithm parameters. In: Swarm intelligence: proceedings of the 7th international conference. ANTS 2010. Lecture notes in computer science, vol 6234. Springer, Berlin, pp 203–214

    Google Scholar 

Download references

Acknowledgments

Support from colleagues from the Research Group on Computational Logistics of Department of Mathematical Information Technology (University of Jyväskylä) is gratefully acknowledged. Office for Jussi Rasku at University Consortium of Seinäjoki researcher residency was supported by European Regional Development Fund (ERDF): A31342. Nysret Musliu was supported by the Austrian Science Fund (FWF): P24814-N23. Tommi Kärkkäinen was supported by a research grant of Jenny and Antti Wihuri Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jussi Rasku .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Rasku, J., Musliu, N., Kärkkäinen, T. (2014). Automating the Parameter Selection in VRP: An Off-line Parameter Tuning Tool Comparison. In: Fitzgibbon, W., Kuznetsov, Y., Neittaanmäki, P., Pironneau, O. (eds) Modeling, Simulation and Optimization for Science and Technology. Computational Methods in Applied Sciences, vol 34. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9054-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-9054-3_11

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-9053-6

  • Online ISBN: 978-94-017-9054-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics