Mixed replenishment policy for ATO supply chain based on hybrid genetic simulated annealing algorithm
type: Item
instance of: scholarly article
title: Mixed replenishment policy for ATO supply chain based on hybrid genetic simulated annealing algorithm
zbMATH Open document ID: 1407.90055
DOI: 10.1155/2014/574827
author: Hui Huang
author: Yan Jin
author: Bo Huang
author: Han-Guang Qiu
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 90B06
Mathematics Subject Classification ID: 90B05
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7016765
Wikidata QID: Q59066143
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/574827
OpenAlex ID: W2007351262
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description: scientific article; zbMATH DE number 7016765