Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Systems Design and Applications

ISDA 2016: Intelligent Systems Design and Applications pp 457–466Cite as

Multi-agent Based Truck Scheduling Using Ant Colony Intelligence in a Cross-Docking Platform

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

The management of trucks in a cross-docking platform is a process under five steps: the arrival, the control, the unloading, the transfer and finally the loading. In each of these steps, a sequence of decisions arise. To achieve an optimal and robust solutions, the interdependencies between the different planning functions should be taken into account, and scheduling decisions must be made simultaneously. The truck scheduling should incorporate a real-time information regarding the resource availability and truck arrival and departure times which are crucial in a cross-docking platform. In this work, we present how the autonomous, distributed, and dynamic nature of the multi-agent paradigm by introducing ant colony intelligence (ACI) can provide a framework for the cooperation of various functions of the cross-dock to develop a robust schedule. The goal of this paper is to find an optimal dynamic scheduling system related to the parking lot and dock operations at the cross-dock facility. The proposed approach represents ACI integrated with both truck agents and resource agents to solve the truck scheduling problem in a dynamic environment.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    truck processing time deviation is considered as a performance measure for a truck scheduling problem as mentioned [12].

References

  1. Amini, A., Tavakkoli-Moghaddam, R.: A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks. Comput. Ind. Eng. 96, 180–191 (2016)

    Article  Google Scholar 

  2. Apte, U.M., Viswanathan, S.: Effective cross docking for improving distribution efficiencies. Int. J. Logistics Res. Appl. 3(3), 291–302 (2000)

    Article  Google Scholar 

  3. Blum, C.: Ant colony optimization: introduction and recent trends. Phys. Life Rev. 2(4), 353–373 (2005)

    Article  Google Scholar 

  4. Boloori Arabani, A., Fatemi Ghomi, S., Zandieh, M.: A multi-criteria cross-docking scheduling with just-in-time approach. Int. J. Adv. Manuf. Technol. 49(5–8), 741–756 (2010)

    Article  Google Scholar 

  5. Bonney, J.: Non-stop Logistics 36(5), 63–64 (1994)

    Google Scholar 

  6. Boysen, N., Fliedner, M.: Cross dock scheduling: classification, literature review and research agenda. Omega 38(6), 413–422 (2010)

    Article  Google Scholar 

  7. Chen, W.N., Zhang, J.: Ant colony optimization for software project scheduling and staffing with an event-based scheduler. IEEE Trans. Softw. Eng. 39(1), 1–17 (2013)

    Article  Google Scholar 

  8. Ferber, J.: Les Systàmes Multi Agents: Vers une intelligence collective. France, intereditions edn, Paris (1995)

    Google Scholar 

  9. Gue, K., Kang, K.: Staging queues in material handling and transportation systems. In: Proceedings of the Winter Simulation Conference, vol. 2, pp. 1104–1108 (2001)

    Google Scholar 

  10. Heidari, F., Zegordi, S.H., Tavakkoli-Moghaddam, R.: Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach. J. Intell. Manuf. pp. 1–16 (2015)

    Google Scholar 

  11. Konur, D., Golias, M.M.: Analysis of different approaches to cross-dock truck scheduling with truck arrival time uncertainty. Comput. Industr. Eng. 65(4), 663–672 (2013)

    Article  Google Scholar 

  12. Ladier, A.L., Alpan, G.: Optimisation des operations dans une plateforme logistique: prise en compte des flux d arrivee et de la capacite des ressources internes (2013)

    Google Scholar 

  13. Ladier, A.L., Alpan, G.: Crossdock truck scheduling with time windows: earliness, tardiness and storage policies. J. Intell. Manuf. pp. 1–15 (2014)

    Google Scholar 

  14. Ladier, A.L., Alpan, G., Greenwood, A.G.: Robustness evaluation of an IP-based cross-docking schedule using discrete-event simulation. In: Industrial and Systems Engineering Research Conference. p. I211. Canada (2014)

    Google Scholar 

  15. Marcotte, S., Durand, M., Crainic, T.G.: Amenagement et gestion des flux de la cour d’un centre de distribution: une etude de cas (2013)

    Google Scholar 

  16. Schaffer, B.: Cross docking can increase efficiency. Automatic I.D. News 14(8), 34–37 (1998)

    Google Scholar 

  17. Shakeri, M., Low, M.Y.H., Turner, S.J., Lee, E.W.: A robust two-phase heuristic algorithm for the truck scheduling problem in a resource-constrained crossdock. Comput. Oper. Res. 39(11), 2564–2577 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  18. Stephan, K., Boysen, N.: Cross-docking. J. Manag. Control 22(1), 129–137 (2011)

    Article  Google Scholar 

  19. Wang, L., Wang, Z., Hu, S., Liu, L.: Ant colony optimization for task allocation in multi-agent systems. China Commun. 10(3), 125–132 (2013)

    Article  Google Scholar 

  20. Xiang, W., Lee, H.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Eng. Appl. Artif. Intell. 21(1), 73–85 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

These research and innovation are carried out as part of a MOBIDOC Phd thesis as part of PASRI program funded by the EU and administered by the ANPR.

Author information

Authors and Affiliations

  1. Laboratory SOIE, Higher Institute of Management of Tunis, University of Tunis, Le Bardo, Tunis, Tunisia

    Houda Zouhaier & Lamjed Ben Said

Authors
  1. Houda Zouhaier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lamjed Ben Said
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Houda Zouhaier .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Zouhaier, H., Ben Said, L. (2017). Multi-agent Based Truck Scheduling Using Ant Colony Intelligence in a Cross-Docking Platform. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-53480-0_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation