Skip to main content
Springer Nature Link
Log in

A Memetic Algorithm for the Team Orienteering Problem

  • Chapter
  • First Online:
Business and Consumer Analytics: New Ideas

Abstract

The Team Orienteering Problem (TOP) is an expansion of the orienteering problem. The problem’s data is a set of nodes and each node is associated with a score value. The goal of the TOP is to construct a discrete number of routes in order to visit the nodes and collect their scores aiming to maximize the total collected score with respect to a total travel time constraint. In this paper we propose a Memetic algorithm with Similarity Operator (\(\operatorname {MSO-TOP}\)) for solving the TOP. The concept of the “similarity operator” is that feasible sub-routes of the solutions are serving as chromosomes. The efficacy of \(\operatorname {MSO-TOP}\) was tested using the known benchmark instances for the TOP. From the experiments it was concluded that “similarity operator” is a promising technique and \(\operatorname {MSO-TOP}\) produces quality solutions.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Archetti, C., Hertz, A., & Speranza, M. G. (2007). Metaheuristics for the team orienteering problem. Journal of Heuristics, 13(1), 49–76.

    Article  Google Scholar 

  2. Bonnefoy, L. (2010). L’optimisation par essaims particulaires appliquée au team orienteering problem. Preprint available at: http://ludovicbonnefoy.files.wordpress.com/2010/10/majecstic2010.pdf.

  3. Bouly, H., Dang, D. C., & Moukrim, A. (2010). A memetic algorithm for the team orienteering problem. 4OR, 8(1), 49–70.

    Article  Google Scholar 

  4. Butt, S. E., & Cavalier, T. M. (1994). A heuristic for the multiple tour maximum collection problem. Computers & Operations Research, 21(1), 101–111.

    Article  Google Scholar 

  5. Chao, I. M., Golden, B. L., & Wasil, E. A. (1996). The team orienteering problem. European journal of operational research, 88(3), 464–474.

    Article  Google Scholar 

  6. Chao, I. M., Golden, B. L., & Wasil, E. A. (1996). A fast and effective heuristic for the orienteering problem. European Journal of Operational Research, 88(3), 475–489.

    Article  Google Scholar 

  7. Dang, D. C., Guibadj, R. N., & Moukrim, A. (2011). A PSO-based memetic algorithm for the team orienteering problem. Applications of Evolutionary Computation, Springer Berlin Heidelberg, 471–480.

    Google Scholar 

  8. Dang, D. C., Guibadj, R. N., & Moukrim, A. (2013). An effective PSO-inspired algorithm for the team orienteering problem. European Journal of Operational Research, 229(2), 332–344.

    Article  Google Scholar 

  9. Desrosiers, J., & Lübbecke, M. E. (2005). A primer in column generation, Springer US, 1–32.

    Google Scholar 

  10. Golden, B. L., Levy, L., & Vohra, R. (1987). The orienteering problem. Naval research logistics, 34(3), 307–318.

    Article  Google Scholar 

  11. Hart, W. E., Krasnogor, N., & Smith, J. E. (Eds.). (2004). Recent advances in memetic algorithms, Springer Science & Business Media, 166.

    Google Scholar 

  12. Ke, L., Archetti, C. & Feng, Z. (2008). Ants can solve the team orienteering problem. Computers & Industrial Engineering, 54(3), 648–665.

    Article  Google Scholar 

  13. Kim, B. I., Li, H., & Johnson, A. L. (2013). An augmented large neighborhood search method for solving the team orienteering problem. Expert Systems with Applications, 40(8), 3065–3072.

    Article  Google Scholar 

  14. Lin, S. W. (2013). Solving the team orienteering problem using effective multi-start simulated annealing. Applied Soft Computing, 13(2), 1064–1073.

    Article  Google Scholar 

  15. Marinakis, Y., Politis, M., Marinaki, M., & Matsatsinis, N. (2015). A Memetic-GRASP Algorithm for the Solution of the Orienteering Problem. Modelling, Computation and Optimization in Information Systems and Management Sciences, Springer International Publishing, 105–116.

    Google Scholar 

  16. Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989.

    Google Scholar 

  17. Moscato, P., & Cotta, C. (2003). A gentle introduction to memetic algorithms. Handbook of metaheuristics, Springer US, 105–144.

    Google Scholar 

  18. Muthuswamy, S., & Lam, S. (2011). Discrete particle swarm optimization for the team orienteering problem. Memetic Computing, 3(4), 287–303.

    Article  Google Scholar 

  19. Rosenkrantz, D. J., Stearns, R. E., & Lewis, II, P. M. (1977). An analysis of several heuristics for the traveling salesman problem. SIAM journal on computing, 6(3), 563–581.

    Article  MathSciNet  Google Scholar 

  20. Souffriau, W., Vansteenwegen, P., Berghe, G. V., & Van Oudheusden, D. (2010). A path relinking approach for the team orienteering problem.Computers & Operations Research, 37(11), 1853–1859.

    MathSciNet  MATH  Google Scholar 

  21. Souffriau, W., Vansteenwegen, P., Vertommen, J., Berghe, G. V.,& Oudheusden, D. V. (2008). A personalized tourist trip design algorithm for mobile tourist guides. Applied Artificial Intelligence, 22(10), 964–985.

    Article  Google Scholar 

  22. Tang, H., & Miller-Hooks, E. (2005). A tabu search heuristic for the team orienteering problem. Computers & Operations Research, 32(6), 1379–1407.

    Article  Google Scholar 

  23. Toth, P., & Vigo, D. (Eds.). (2014). Vehicle routing: problems, methods, and applications (Vol. 18). Siam.

    Google Scholar 

  24. Vansteenwegen, P., Souffriau, W., & Van Oudheusden, D. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.

    Article  MathSciNet  Google Scholar 

  25. Vansteenwegen, P., Souffriau, W., Berghe, G. V., & Van Oudheusden, D. (2009). A guided local search metaheuristic for the team orienteering problem. European Journal of Operational Research, 196(1), 118–127.

    Article  Google Scholar 

  26. Vansteenwegen, P., Souffriau, W., Berghe, G. V., & Van Oudheusden, D. (2009). Metaheuristics for tourist trip planning. Metaheuristics in the Service Industry, Springer Berlin Heidelberg, 15–31.

    Google Scholar 

  27. Vansteenwegen, P., & Van Oudheusden, D. (2007). The mobile tourist guide: an OR opportunity. OR Insight, 20(3), 21–27.

    Article  Google Scholar 

  28. Vincent, F. Y., Lin, S. W., & Chou, S. Y. (2010). The museum visitor routing problem. Applied Mathematics and Computation, 216(3), 719–729.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technical University of Crete, School of Production Engineering and Management, Chania, Greece

    Dimitra Trachanatzi, Eleftherios Tsakirakis, Magdalene Marinaki, Yannis Marinakis & Nikolaos Matsatsinis

Authors
  1. Dimitra Trachanatzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleftherios Tsakirakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Magdalene Marinaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yannis Marinakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nikolaos Matsatsinis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yannis Marinakis .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Trachanatzi, D., Tsakirakis, E., Marinaki, M., Marinakis, Y., Matsatsinis, N. (2019). A Memetic Algorithm for the Team Orienteering Problem. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-06222-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06221-7

  • Online ISBN: 978-3-030-06222-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics