Skip to main content
SpringerLink
Log in

A Cooperative Multi Colony Ant Optimization Based Approach to Efficiently Allocate Customers to Multiple Distribution Centers in a Supply Chain Network

  • Conference paper
Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3483))

Included in the following conference series:

Abstract

With the rapid change of world economy, firms need to de ploy alternative methodologies to improve the responsiveness of supply chain. The present work aims to minimize the workload disparities among various distribution centres with an aim to minimize the total shipping cost. In general, this problem is characterized by its combinatorial nature and complex allocation criterion that makes its computationally intractable. In order to optimally/near optimally resolve the balanced allocation problem, an evolutionary Cooperative Multi Colony Ant Optimization (CMCAO) has been developed. This algorithm takes its gov erning traits from the traditional Ant Colony optimization (ACO). The proposed algorithm is marked by the cooperation among “sister ants” that makes it compatible to the problems pertaining to multiple dimensions. Robustness of the proposed algorithm is authenticated by com paring with GA based strategy and the efficiency of the algorithm is validated by ANOVA.

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 139.00
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.

References

  1. Aikens, C.H.: X Facility Location Models for distribution planning. European Journal of Operations Research 22, 263–279 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  2. Current, J.R., Min, H., Schilling, D.A.: Multiobjective analysis of location decisions. European Journal of Operational Research 49, 295–307 (1990)

    Article  MATH  Google Scholar 

  3. Dorigo, M., Maniezzo, V., Colorni, A.: The ant systems: optimization by a colony of cooperative agents. IEFE-Trans. Man, Machine and Cybernatics-Part B 26(1), 29–41 (1996)

    Article  Google Scholar 

  4. Maniezzo, V., Colorni, A., Dorigo, M.: Algodesk: an Experimental Comparison of Eight Evolutionary Heuristics Applied to the Quadratic Assignment Problem. European Journal of Operation Research 181, 188–205 (1995)

    Article  Google Scholar 

  5. Kumar, R., Tiwari, M.K., Shankar, R.: Shedulingof Flexible Manufacturing Systems: an ant colony optimization approach. I. Mech. E., Part B., Journal of Engineering Manufacture 217, 1443–1453 (2003)

    Article  Google Scholar 

  6. Gengui, Z., Hokey, M., Mitsuo, G.: The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. Computers and Industrial Engineering 43, 251–261 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK, LE11 3TU

    Srinivas & J. A. Harding

  2. Department of Manufacturing Engineering, NIFFT, Ranchi, 834005, India

    Yogesh Dashora, Alok Kumar Choudhary & M. K. Tiwari

Authors
  1. Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yogesh Dashora
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alok Kumar Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. A. Harding
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. K. Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore

    Heow Pueh Lee

  6. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  7. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  8. OptimaNumerics Ltd, Belfast, United Kingdom

    Chih Jeng Kenneth Tan

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Srinivas, Dashora, Y., Choudhary, A.K., Harding, J.A., Tiwari, M.K. (2005). A Cooperative Multi Colony Ant Optimization Based Approach to Efficiently Allocate Customers to Multiple Distribution Centers in a Supply Chain Network. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_72

Download citation

  • DOI: https://doi.org/10.1007/11424925_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25863-6

  • Online ISBN: 978-3-540-32309-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation