Advertisement

Dynamic Delivery Plan Adaptation in Open Systems

  • Miguel Ángel Rodríguez-García
  • Alberto FernándezEmail author
  • Holger Billhardt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11327)

Abstract

Open fleets offer a dynamic environment where the fleet is continually rebuilt ad-hoc since vehicles can enter or leave the fleet anytime, and the only immutable entity is the item to be delivered. Therefore, we need to be able to define a changeable delivery plan capable of adapting to such a dynamic environment. Hence, we propose Open Fleet Management, a Self-Management platform capable of optimizing plan delivery dynamically. The platform utilizes information about location, routes, delivery in transit and delivery costs to change the shipment plan according to the available carrier. Therefore, if two carriers are doing a shipment service to the same place, the platform will be able to discover such a situation and put them in contact to optimize the efficiency of the shipment.

Keywords

Multi-agent systems Delivery Intelligent transportation systems Semantic technologies Open systems 

Notes

Acknowledgments

Work partially supported by the Autonomous Region of Madrid (grants “MOSI-AGIL-CM” (S2013/ICE-3019) co-funded by EU Structural Funds FSE and FEDER and Talent Attraction Program (“2017-T2/TIC-5664”)), project “SURF” (TIN2015-65515-C4-4-R (MINECO/FEDER)) funded by the Spanish Ministry of Economy and Competitiveness, and through the Excellence Research Group GES2ME (Ref. 30VCPIGI05) co-funded by URJC-Santander Bank.

References

  1. 1.
    Rodrigue, J.P., Comtois, C., Slack, B.: The Geography of Transport Systems. Routledge, New York (2009)Google Scholar
  2. 2.
    Monge, G.: Mémoire sur la théorie des déblais et des remblais. Histoire de l’Académie Royale des Sciences de Paris (1781)Google Scholar
  3. 3.
    Wang, W.W., Zhang, M., Zhou, M.: Using LMDI method to analyze transport sector CO2 emissions in China. Energy 36(10), 5909–5915 (2011)CrossRefGoogle Scholar
  4. 4.
    Liu, Y., Wei, L.: The optimal routes and modes selection in multimodal transportation networks based on improved A* algorithm. In: 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), pp. 236–240. IEEE, April 2018Google Scholar
  5. 5.
    Andreica, M.I., Briciu, S., Andreica, M.E.: Algorithmic solutions to some transportation optimization problems with applications in the metallurgical industry. arXiv preprint arXiv:0903.3622 (2009)
  6. 6.
    Herrero, R., Villalobos, A.R., Cáceres-Cruz, J., Juan, A.A.: Solving vehicle routing problems with asymmetric costs and heterogeneous fleets. Int. J. Adv. Oper. Manage. 6(1), 58–80 (2014)Google Scholar
  7. 7.
    Billhardt, H., et al.: Coordinating open fleets. A taxi assignment example. AI Commun. 30(1), 37–52 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Burt, C.N., Caccetta, L.: Match factor for heterogeneous truck and loader fleets. Int. J. Min. Reclam. Environ. 21(4), 262–270 (2007)CrossRefGoogle Scholar
  9. 9.
    Rodríguez-García, M.Á., Fernández, A., Billhardt, H.: Provider recommendation in heterogeneous transportation fleets. In: Bajo, J. (ed.) PAAMS 2018. CCIS, vol. 887, pp. 416–427. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-94779-2_36CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Rey Juan CarlosMadridSpain

Personalised recommendations