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Mathematical Problems in Engineering
Volume 2014, Article ID 574827, 9 pages
Research Article

Mixed Replenishment Policy for ATO Supply Chain Based on Hybrid Genetic Simulated Annealing Algorithm

1School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
2School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK

Received 22 December 2013; Revised 23 February 2014; Accepted 24 February 2014; Published 27 March 2014

Academic Editor: Erik Cuevas

Copyright © 2014 Hui Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Timely components replenishment is the key to ATO (assemble-to-order) supply chain operating successfully. We developed a production and replenishment model of ATO supply chain, where the ATO manufacturer adopts both JIT and (Q, r) replenishment mode simultaneously to replenish components. The ATO manufacturer’s mixed replenishment policy and component suppliers’ production policies are studied. Furthermore, combining the rapid global searching ability of genetic algorithm and the local searching ability of simulated annealing algorithm, a hybrid genetic simulated annealing algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is given to illustrate the rapid convergence of the HGSAA and the good quality of optimal mixed replenishment policy obtained by the HGSAA. Finally, by comparing the HGSAA with GA, it is proved that the HGSAA is a more effective and reliable algorithm than GA for solving the optimization problem of mixed replenishment policy for ATO supply chain.