A cooperative multi colony ant optimization based approach to efficiently allocate customers to multiple distribution centers in a supply chain network

Authors:
Srinivas;Yogesh Dashora;Alok Kumar Choudhary;J. A. Harding;M. K. Tiwari
Affiliations:
Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK;Department of Manufacturing Engineering, NIFFT, Ranchi, India;Department of Manufacturing Engineering, NIFFT, Ranchi, India;Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, UK;Department of Manufacturing Engineering, NIFFT, Ranchi, India
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

Citing 2
Cited 1

The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach

Computers and Industrial Engineering - Supply chain management
Ant system: optimization by a colony of cooperating agents

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An interactive particle swarm optimisation for selecting a product family and designing its supply chain

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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.