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.
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© 2005 Springer-Verlag Berlin Heidelberg
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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
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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
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