Skip to main content

Logistics Optimization Service Improved with Artificial Intelligence

  • Chapter
Soft Computing for Intelligent Control and Mobile Robotics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 318))

  • 1558 Accesses

Abstract

Today the issue of logistics is a very important within companies to the extent that some have departments devoted exclusively to it. This has evolved over time and today is a fundamental aspect in the fight business seeking to consolidate or remain leaders in their field. With the above we know that logistics can be divided into different classes, however, in this regard, our study is based on the timely distribution to the customer with a lower cost, higher sales and better utilization of space resulting in excellent service. Finally, prepare a comparative analysis of the results with respect to another method of optimization solution space.

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

Access this chapter

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ajith, A., et al. (eds.): Swarm Intelligence in Data Mining. SCI, vol. 34, p. 270. Springer, Berlin (September 2006) ISBN: 3-540-34955-3

    MATH  Google Scholar 

  2. Lourdes, A., Carlos, C.: Algoritmos Evolutivos: un enfoque practico. In: Grupo, A. (ed.) Primera Edicion, México (2007)

    Google Scholar 

  3. Alan, C.M.E., Jaime, M.A., Manuel, A.R.J., Calleros, G., Rafael, R., Antonio, D.C.: Acceso a Repositorios de Objetos de Aprendizaje a Travès de un Sistema de Gestión de Contenidos. In: Conferencia Conjunta Iberoamericana sobre Tecnologías de Aprendizaje (CCITA 2009), July 6-10, Mèrida Yucatán, México (2009)

    Google Scholar 

  4. Cowan George, S., Reynolds Robert, G.: Acquisition of Software engineering knowledge, vol. 14. World Scientific, Singapore

    Google Scholar 

  5. Dasarathy/Belur, V.: Data Mining and Knowledge Discovery: Theory, Tools, and Technology, Orlando (Aprill 2001)

    Google Scholar 

  6. Gill, S., et al.: Data Wherehousing. La integracion de la informacilon para la mejor toma de decisions. Prentice Hall, Mexico (1996)

    Google Scholar 

  7. Landa-Becerra, R.: Uso de Informacón del Dominio para Mejorar el Desempeño de un Algoritmo Evolutivo. CINVESTV PhD Thesis (2007)

    Google Scholar 

  8. Muñoz, A., Hernandez, A., Villa, E.: Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2005, pp. 209–216. Association for Computing Machinery, Newyork (2005)

    Google Scholar 

  9. Jaime, M.A., Rene, S.S., John Squires, A.R.-g., Ricardo, A., Ricardo, M.G.: Aprendizaje Multiculturales. In: Topicos selectos de Tecnologia Educativa, Universidad de Colima, Compilador, Acosta Ricardo (April 2010)

    Google Scholar 

  10. Ochoa, A., Gonzalez, S.: Simulación Social de una Sociedad Artificial basada en Algoritmos Culturales. International Journal of South American Archeology (IJSA 2011-0626) (2009)

    Google Scholar 

  11. Juan, P.: Méodos y Modelos de Investigación de Operaciones: Modelos Determinísticos, vol. 1. Limusa

    Google Scholar 

  12. Reynolds, G.R., Sverdlink, W.: Problem Solving Using Cultural Algorithms. In: International Conference on Evolutionary Computation (1994)

    Google Scholar 

  13. Reynolds, G.R., Peng, B., Whallon, R.: Emergent Social Structures in Cultural Algorithms (2008)

    Google Scholar 

  14. Carlos, R.L.J.: Uso de la minería de datos con fines predictorios de la infraestructura de seguridad de redes Monterrey, N. L (2004)

    Google Scholar 

  15. Taha, H.: Investigación de Operaciones. Séptima edición, México D.F., pp. 71–90. Prentice Hall, Englewood Cliffs (2004)

    Google Scholar 

  16. Ian, W.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier, Amsterdam

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ochoa, A., Garcia, Y., Yañez, J. (2010). Logistics Optimization Service Improved with Artificial Intelligence. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15534-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15533-8

  • Online ISBN: 978-3-642-15534-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics