Research Article | OPEN ACCESS
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
1, 2Feipeng Guo and 3Qibei Lu
1School of Business Administration, Zhejiang Gongshang University
2Information Technology Department, Zhejiang Economic and Trade Polytechnic,
Hangzhou, 310018, China
3Department of Business Administration, College of Taizhou Vocational
and Technical, Taizhou, 318000, China
Advance Journal of Food Science and Technology 2013 10:1285-1291
Received: April 26, 2013 | Accepted: May 07, 2013 | Published: October 05, 2013
Abstract
With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.
Keywords:
Agricultural supply chain, BP neural network, partner selection, principal component analysis, rough set,
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.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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