Collaborating Multiple 3PL Enterprises for Ontology-Based Interoperable Transportation Planning

  • Muhammad Ali Memon
  • Agnès Letouzey
  • Mohammed Hedi Karray
  • Bernard Archimède
Conference paper
Part of the Proceedings of the I-ESA Conferences book series (IESACONF, volume 7)

Abstract

Today enterprises have to distribute their final products to far away consumers. It is difficult and not cost effective for these enterprises to manage their own transport vehicles. Thus, they outsource their transportation tasks to third party logistics (3PL) companies. These 3PL companies take transport orders from several clients and try to group them in the vehicles to utilize their resources at maximum. An issue of interoperability arises, when 3PL companies have to process different transport orders arriving from several clients in different formats and terminologies. Secondly, how 3PLS will collaborate with other 3PL companies following different working standards and also for collaboratively delivering transport orders which single 3PL cannot deliver alone due to its limited operational geographic area. Interoperability to achieve collaborative transportation planning is our concern in the context of this paper. Interoperability is a key issue for collaboration, especially in case of heterogeneous environment, when entities trying to collaborate have different ways of functioning and follow certain standards specific to their organizations. So the objective of this paper is to present a distributed and interoperable architecture for planning transportation activities of multiple logistics enterprises aiming at a better use of transport resources and by grouping transport orders of several manufacturers for each effective displacement.

Keywords

Interoperable and distributed scheduling Multi-agent systems Collaborative transportation planning Third party logistics Ontology 

References

  1. 1.
    Marasco, A. (2008). Third-party logistics: A literature review. International Journal of Production Economics, 113(1), 127–147.CrossRefGoogle Scholar
  2. 2.
    Zbib, N., Archimède, B., & Charbonnaud, P. (2012). Interoperability service utility model and its simulation for improving the business process collaboration. In Enterprise Interoperability V (pp. 403–413). London: Springer.Google Scholar
  3. 3.
    Karray, M. H., Morello B., & Zerhouni N. (2010). A contextual semantic mediator for a distributed cooperative maintenance platform. IEEE INDIN Conference (pp. 181–188).Google Scholar
  4. 4.
    Sauer, J., & Appelrath, H. (2000). Integrating transportation in a multi-site scheduling environment. IEEE System Sciences. Proceedings of the 33rd Annual Hawaii International Conference. 9 pp.Google Scholar
  5. 5.
    Baykasoglu, A., & Kaplanoglu, V. (2011). A multi-agent approach to load consolidation in transportation. Advances in Engineering Software, 42(7), 477–490.Google Scholar
  6. 6.
    Takoudjou, R. et al. (2012). A Hybrid multistart heuristic for the pickup and delivery problem with and without transshipment 9th International Conference on Modeling, Optimization and SIMulation, 2012.Google Scholar
  7. 7.
    Sprenger, R., & Mönch, L. (2008, December). A simulation framework for assessing the performance of cooperative transportation planning algorithms. In Proceedings of the 40th conference on winter simulation (pp. 2769–2776). Winter Simulation Conference.Google Scholar
  8. 8.
    Mes, M., van der Heijden, M., & Schuur, P. (2010). Interaction between intelligent agent strategies for realtime transportation planning. Central European Journal of Operations Research, 203(1), 1–22.Google Scholar
  9. 9.
    Kozlak, J., Créput, J., Hilaire, V., & Koukam, A. (2004). Multi-agent environment for dynamic transport planning and scheduling. Computational Science-ICCS, 2004, 638–645.Google Scholar
  10. 10.
    Niaraki, A. S., & Kim, K. (2009). Ontology based personalized route planning system using a multi-criteria decision making approach. Expert Systems with Applications, 36, 2250–2259.CrossRefGoogle Scholar
  11. 11.
    Davidsson, P., Ramstedt, L., & Törnquist, J. (2005). Inter-Organization Interoperability in Transport Chains Using Adapters Based on Open Source Freeware. Interoperability of enterprise software and applications, Berlin, Germany: Springer Verlag.Google Scholar
  12. 12.
    Becker, M. A., & Smith, S. F. (1998). An ontology for multi-modal transportation planning and scheduling. The Robotics Institute: Carnegie Mellon University.Google Scholar
  13. 13.
    Smith, S. F., Cortellessa, G., Hildum, D. W., & Ohler, C. M. (2004b) “Using a Scheduling Domain Ontology to Compute User-Oriented Explanations”, In Planning and Scheduling. In L. Castillo, D. Borrajo, M. A. Salido & A. Oddi (Eds.), Frontiers in artificial intelligence and applications series, IOS Press, Forthcoming.Google Scholar
  14. 14.
    Memon, M. A., & Archimède, B. (2013). Towards a distributed framework for transportation planning: A food supply chain case study. IEEE International Conference on Networking, Sensing and Control, France.Google Scholar
  15. 15.
    Memon, M. A., & Archimede, B. (2013). A Multi-Agent Distributed Framework for Collaborative Transportation Planning in IFACE Conference on Manufacturing Modelling, Management and Control, MIM 2013 Saint Petersburg, Russia.Google Scholar
  16. 16.
    Song, F., Zacharewicz, G., & Chen, D. (2013). An ontology-driven framework towards building enterprise semantic information layer. Advanced Engineering Informatics, 27(1), 38–50.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Muhammad Ali Memon
    • 1
  • Agnès Letouzey
    • 1
  • Mohammed Hedi Karray
    • 1
  • Bernard Archimède
    • 1
  1. 1.University of Toulouse, INP-ENITTarbes CedexFrance

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