基于优化支持向量机的供应链竞争力评价方法

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 27-31.

基于优化支持向量机的供应链竞争力评价方法

钟夫,郭建胜,张斯嘉,王族统

空军工程大学装备管理与安全工程学院西安710051,空军工程大学装备管理与安全工程学院西安710051,空军工程大学装备管理与安全工程学院西安710051,空军工程大学装备管理与安全工程学院西安710051

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受陕西省自然科学基金项目:两阶段不确定规划问题中的信息价值研究(2013JM1003)资助

Supply Chain Competitiveness Evaluation Method Based on Optimized Support Vector Machine

ZHONG Fu, GUO Jian-sheng, ZHANG Si-jia and WANG Zu-tong

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 供应链竞争力变量因素多、信息量少、数据收集困难,造成其难以被准确评价。针对该问题构建了一个新的供应链评价指标体系,提出了一种新的供应链竞争力评价方法。它利用蜂群算法全局优化能力强的优点,对支持向量机的控制参数进行有效优化,以此为基础,构建了ABC-SVM评价模型。实验结果表明,所提方法能够有效提高供应链竞争力的评价精度,对提高企业决策效率具有积极意义。

关键词: 供应链竞争力,支持向量机,人工蜂群算法,参数优化,评价模型

Abstract: Aiming at that the supply chain is difficult to accurately evaluate,it comes from more variable facters,less amout of information and the difficult data collection.This paper built a new evaluation index system of supply chain and proposed a new supply chain competitiveness evaluation method.It uses the advantages of global optimization ability of artificial bee colony algorithm to optimize the control parameters of support vector machine effectively,and on this basis,the ABC-SVM evaluation model is constructed.The experimental results show that the new proposed method can effectively improve the evaluation presision of the supply chain competitiveness,and has a positive meaning to improve business decision-making effctiveness.

Key words: Supply chain competitiveness,Support vector machine,Artificial bee colony algorithm,Parameter optimization,Evaluation model

钟夫,郭建胜,张斯嘉,王族统. 基于优化支持向量机的供应链竞争力评价方法[J]. 计算机科学, 2015, 42(Z11): 27-31. https://doi.org/

ZHONG Fu, GUO Jian-sheng, ZHANG Si-jia and WANG Zu-tong. Supply Chain Competitiveness Evaluation Method Based on Optimized Support Vector Machine[J]. Computer Science, 2015, 42(Z11): 27-31. https://doi.org/

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