摘要
针对协同创新过程中不同阶段的特征,基于支持向量机(SVM)与直觉模糊集理论,构建了创新合作伙伴的两阶段选择指标体系及"初选——精选与优化"决策模型。初选阶段,针对初选合作指标体系,利用SVM缩小创新合作伙伴的选择范围;精选与优化阶段,在充分考虑协同创新过程中的创新伙伴多属性、群体决策性等特征的基础上,利用直觉模糊集TOPSIS法,最终确定协同创新合作伙伴。最后,通过对某医药企业创新合作伙伴选择的算例分析,证明了该决策模型的可行性,为协同创新合作伙伴的优选问题提供新的决策思路与方法。
This study establishes a two-stage selection indexing system based on partners in innovation and a deci- sion-making model on the basis of ＂primary selection, fine selection, and optimization,＂ in accordance with the features of different stages of collaborative innovation on the basis of support vector machine（SVM） theories and intuitionistic fuzzy sets. In the primary selection stage, SVM was used to reduce the selection range of partners in innovation as per the cooperation indexing system of the primary selection. In the stage of fine selection and optimization, the intuitionistic fuzzy set TOPSIS method was adopted to determine the final partners in collaborative innovation, and the characteristics of innovation partners in the process of collaborative innovation such as multi-attribute and group decision-making were completely considered. Finally, the feasibility of the decision-making model was depicted by the case analysis of selecting a partner for a pharmaceutical enterprise. This provided innovative decision-making insights and methods for the optimized selection of partners in a collaborative innovation.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2018年第1期179-186,共8页
Journal of Harbin Engineering University
基金
黑龙江省哲学社会科学研究规划项目(16JYC02)
黑龙江工程学院青年科学基金项目(2014QJ15)
黑龙江省自然科学基金青年项目(JJ2016QN0645)
关键词
支持向量机
直觉模糊集
TOPSIS
创新合作伙伴
决策
support vector machine （SVM）
intuitionistic fuzzy sets
TOPSIS
partners in innovation
decision-making
