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     Advance Journal of Food Science and Technology


Research on Genetic Optimization for Multinational Food Companies with Random Lead Time

1, 2Wen Zhang and 1Yaming Zhang
1School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
2School of Science and Technology, Gannan Normal University, Ganzhou 341000, China
Advance Journal of Food Science and Technology  2014  8:956-960
http://dx.doi.org/10.19026/ajfst.6.139  |  © The Author(s) 2014
Received: March ‎14, ‎2014  |  Accepted: April ‎15, ‎2014  |  Published: August 10, 2014

Abstract

This study aims to investigate the inventory optimization problems for multinational food companies with random lead time. Multinational organizations play an increasingly important role in the world economy. The majority of their activities seem to be driven by market-seeking considerations. In this study we considered a multinational organization in which lead times are stochastic. There is a determined due date that if demands are not prepared for delivery in that day, backlogging cost would happen. In contrast to most of studies in this field that consider sum of these cost, this study attempts to make a tradeoff between these objectives' affects using multi- objective approach based on genetic algorithm. Furthermore, effectiveness of our proposed method is compared against an adapted non-dominated sorting genetic algorithm which has been presented for this problem.

Keywords:

Genetic algorithm, inventory optimization, multinational food company, random lead time,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2042-4876
ISSN (Print):   2042-4868
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