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

  • Srinivas
  • Yogesh Dashora
  • Alok Kumar Choudhary
  • J. A. Harding
  • M. K. Tiwari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3483)

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.

Keywords

Supply chain Balanced allocation Cooperative Multi Colony Ants ANOVA 

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References

  1. 1.
    Aikens, C.H.: X Facility Location Models for distribution planning. European Journal of Operations Research 22, 263–279 (1985)zbMATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Current, J.R., Min, H., Schilling, D.A.: Multiobjective analysis of location decisions. European Journal of Operational Research 49, 295–307 (1990)zbMATHCrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Srinivas
    • 1
  • Yogesh Dashora
    • 2
  • Alok Kumar Choudhary
    • 2
  • J. A. Harding
    • 1
  • M. K. Tiwari
    • 2
  1. 1.Wolfson School of Mechanical and Manufacturing EngineeringLoughborough UniversityUK
  2. 2.Department of Manufacturing EngineeringNIFFTRanchiIndia

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