Skip to main content
SpringerLink
Log in

Optimization of the p-Hub Median Problem via Artificial Immune Systems

  • Conference paper
  • First Online:
Book cover Computational Logistics (ICCL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11756))

Included in the following conference series:

  • 1202 Accesses

Abstract

Recent advances in logistics, transportation and in telecommunications offer great opportunities to citizens and organizations in a globally-connected world, but they also arise a vast number of complex challenges that decision makers must face. In this context, a popular optimization problem with practical applications to the design of hub-and-spoke networks is analyzed: the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) where a fixed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub and the number of hubs is known in advance. An immune inspired metaheuristic is proposed to solve the problem in deterministic scenarios. In order to show its efficiency, a series of computational tests are carried out using small and large size instances from the Australian Post dataset with node sizes up to 200. The results contribute to a deeper understanding of the effectiveness of the employed metaheuristic for solving the USApHMP in small and large networks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Notes

  1. 1.

    Unlike in nature, there is no distinction for the terms antibodies/lymphocytes/cells in the context of AISs.

References

  1. Alvarez Fernandez, S.: A metaheuristic and simheuristic approach for the p-hub median problem from a telecommunication perspective. Ph.D. thesis, University of Brasília (2018). http://hdl.handle.net/10803/666752

  2. Alvarez Fernandez, S., Fantinato, D.G., Montalvao, J., Attux, R., Silva, D.G.: Immune-inspired optimization with autocorrentropy function for blind inversion of Wiener systems. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2018)

    Google Scholar 

  3. Alvarez Fernandez, S., Ferone, D., Juan, A.A., Silva, D., Armas, J.: A 2-stage biased-randomized iterated local search for the uncapacitated single-allocation p-hub median problem. Trans. Emerging Telecommun. Technol. 29(9), e3418 (2018)

    Article  Google Scholar 

  4. Amin-Naseri, M.R., Yazdekhasti, A., Salmasnia, A.: Robust bi-objective optimization of uncapacitated single allocation p-hub median problem using a hybrid heuristic algorithm. Neural Comput. Appl. 29(9), 511–532 (2018)

    Article  Google Scholar 

  5. Aydin, I., Karakose, M., Akin, E.: A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl. Soft Comput. 11(1), 120–129 (2011)

    Article  Google Scholar 

  6. Bernardino, H.S., Barbosa, H.J.C.: Artificial immune systems for optimization. In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimization, pp. 389–411. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00267-0_14

    Chapter  Google Scholar 

  7. Bhadoria, V.S., Pal, N.S., Shrivastava, V.: Artificial immune system based approach for size and location optimization of distributed generation in distribution system. Int. J. Syst. Assur. Eng. Manag. 10(3), 339–349 (2019)

    Article  Google Scholar 

  8. Burnet, F.M.: Clonal Selection and After. In: Bell, G.I., Perelson, A.S., Pimbley Jr., G.H. (eds.) Theoretical Immunology, pp. 63–85. Marcel Dekker Inc., New York (1978)

    Google Scholar 

  9. de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  10. Diabat, A., Kannan, D., Kaliyan, M., Svetinovic, D.: An optimization model for product returns using genetic algorithms and artificial immune system. Resour. Conserv. Recycl. 74, 156–169 (2013)

    Article  Google Scholar 

  11. Dudek, G.: An artificial immune system for classification with local feature selection. IEEE Trans. Evol. Comput. 16(6), 847–860 (2012)

    Article  Google Scholar 

  12. Ernst, A.T., Krishnamoorthy, M.: Exact and heuristic algorithms for the uncapacitated multiple allocation p-hub median problem. Eur. J. Oper. Res. 104(1), 100–112 (1998)

    Article  Google Scholar 

  13. Filipović, V., Kratica, J., Tošić, D., Dugošija, D.: GA inspired heuristic for uncapacitated single allocation hub location problem. In: Mehnen, J., Köppen, M., Saad, A., Tiwari, A. (eds.) Applications of Soft Computing. Springer, Berlin Heidelberg (2009). https://doi.org/10.1007/978-3-540-89619-7_15

    Chapter  Google Scholar 

  14. Grine, F.Z., Kamach, O., Sefiani, N.: A new efficient metaheuristic for solving the uncapacitated single allocation p-hub median problem. In: 2018 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), pp. 69–74. IEEE (2018)

    Google Scholar 

  15. Ilić, A., Urošević, D., Brimberg, J., Mladenović, N.: A general variable neighborhood search for solving the uncapacitated single allocation p-hub median problem. Eur. J. Oper. Res. 206(2), 289–300 (2010)

    Article  MathSciNet  Google Scholar 

  16. Kratica, J.: An electromagnetism-like metaheuristic for the uncapacitated multiple allocation p-hub median problem. Comput. Ind. Eng. 66(4), 1015–1024 (2013)

    Article  Google Scholar 

  17. Kratica, J., Stanimirović, Z., Tošić, D., Filipović, V.: Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem. Eur. J. Oper. Res. 182(1), 15–28 (2007)

    Article  MathSciNet  Google Scholar 

  18. Leung, C.S.K., Lau, H.Y.K.: A hybrid multi-objective ais-based algorithm applied to simulation-based optimization of material handling system. Appl. Soft Comput. 71, 553–567 (2018)

    Article  Google Scholar 

  19. Li, T., Song, R., He, S., Bi, M., Yin, W., Zhang, Y.: Multiperiod hierarchical location problem of transit hub in urban agglomeration area. Mathematical Problems in Engineering 2017 (2017), Article ID 7189060, 15 pages

    Google Scholar 

  20. Li, T., Song, R., He, S., et al.: Hierarchical model for regional integrated passenger hub layout. J. Beijing Univ. Technol. 40(11), 1700–1706 (2014)

    Google Scholar 

  21. Martí, R., Corberán, Á., Peiró, J.: Scatter search for an uncapacitated p-hub median problem. Comput. Oper. Res. 58, 53–66 (2015)

    Article  MathSciNet  Google Scholar 

  22. Meier, J., Clausen, U.: Solving classical and new single allocation hub location problems on euclidean data. Technical report, Optimization Online (2015)

    Google Scholar 

  23. Meier, J.F., Clausen, U., Rostami, B., Buchheim, C.: A compact linearisation of euclidean single allocation hub location problems. Electron. Notes Discrete Math. 52, 37–44 (2016)

    Article  MathSciNet  Google Scholar 

  24. Milanović, M.: A new evolutionary based approach for solving the uncapacitated multiple allocation p-hub median problem. In: Gao, X.Z., Gaspar-Cunha, A., Köppen, M., Schaefer, G., Wang, J. (eds.) Soft Computing in Industrial Applications. Springer, Berlin Heidelberg (2010). https://doi.org/10.1007/978-3-642-11282-9_9

    Chapter  Google Scholar 

  25. O’kelly, M.E.: A quadratic integer program for the location of interacting hub facilities. Eur. J. Oper. Res. 32(3), 393–404 (1987)

    Article  MathSciNet  Google Scholar 

  26. Peiró, J., Corberán, Á., Martí, R.: Grasp for the uncapacitated r-allocation p-hub median problem. Comput. Oper. Res. 43, 50–60 (2014)

    Article  MathSciNet  Google Scholar 

  27. Porselvi, S., Balaji, A., Jawahar, N.: Artificial immune system and particle swarm optimisation algorithms for an integrated production and distribution scheduling problem. Int. J. Logistics Syst. Manag. 30(1), 31–68 (2018)

    Article  Google Scholar 

  28. Rani, R.: Distributed query processing optimization in wireless sensor network using artificial immune system. In: Mishra, B.B., Dehuri, S., Panigrahi, B.K., Nayak, A.K., Mishra, B.S.P., Das, H. (eds.) Computational Intelligence in Sensor Networks, pp. 1–23. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-662-57277-1_1

    Chapter  Google Scholar 

  29. Rostami, B., Meier, J., Buchheim, C., Clausen, U.: The uncapacitated single allocation p-hub median problem with stepwise cost function. Technical report, Optimization Online (2015)

    Google Scholar 

  30. Silva, D.G., Montalvão, J., Attux, R., Coradine, L.C.: An immune-inspired, information-theoretic framework for blind inversion of Wiener systems. Sig. Process. 113, 18–31 (2015)

    Article  Google Scholar 

  31. Sun, X., Dai, W., Zhang, Y., Wandelt, S.: Finding p-hub median locations: An empirical study on problems and solution techniques. J. Adv. Transp. 2017 (2017), Article ID 9387302, 23 pages

    Google Scholar 

  32. Wakui, T., Hashiguchi, M., Sawada, K., Yokoyama, R.: Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks. Energy 170, 1228–1248 (2019)

    Article  Google Scholar 

  33. Zhang, W., Yen, G.G., He, Z.: Constrained optimization via artificial immune system. IEEE Trans. Cybern. 44(2), 185–198 (2014)

    Article  Google Scholar 

  34. Zhang, Z., Yue, S., Liao, M., Long, F.: Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft. Comput. 18(1), 185–206 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Brasília, Brasilia, Brazil

    Stephanie Alvarez Fernandez & Daniel G. Silva

  2. Exact Sciences Institute, University of Brasília, Brasilia, Brazil

    Gabriel Lins e Nobrega

Authors
  1. Stephanie Alvarez Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriel Lins e Nobrega
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel G. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stephanie Alvarez Fernandez .

Editor information

Editors and Affiliations

  1. Universidad del Norte, Barranquilla, Colombia

    Carlos Paternina-Arboleda

  2. Universität Hamburg, Hamburg, Germany

    Stefan Voß

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alvarez Fernandez, S., Lins e Nobrega, G., Silva, D.G. (2019). Optimization of the p-Hub Median Problem via Artificial Immune Systems. In: Paternina-Arboleda, C., Voß, S. (eds) Computational Logistics. ICCL 2019. Lecture Notes in Computer Science(), vol 11756. Springer, Cham. https://doi.org/10.1007/978-3-030-31140-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31140-7_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31139-1

  • Online ISBN: 978-3-030-31140-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation