模糊未确知环境下研发与转化功能性平台绩效评价方法研究

R&D and transformation functional platform can help high-tech industries effectively gather resources and transform technology and eventually to enhance the overall competitiveness of their industries. However, at present, most platforms are only a kind of "semi-finished products", there are many problems with platform development, such as,the effectiveness needs to be improved, the operational management mechanism still needs to be optimized, the performance evaluation and evaluation mechanism are not perfect and so on. In order to improve platform’s operational performance, this project aims to establishes a BSC model suitable for complex environment, conducts a systematic identifies and evaluates on platform operation performance. First of all, considering the multidimensional factors that affect the operating efficiency of the platform, the BSC system of performance influencing factors is constructed by means of literature analysis and questionnaire survey, and the causal association is analyzed by integrating the BSC and DEMATEL methods. Secondly, considering the complexity of the weight information of performance index, the method to calculate index importance weight based on FANP and index classification weight based on the deviation maximization method are proposed. Third, due to the uncertainty and diversity of performance evaluation, this paper proposes an evaluation strategy based on interval binary linguistic variables and mixed weighted average operator. Fourthly, considering the unperceivability of platform performance indicator information, a comprehensive assessment model based on unascertained measure and a recognition vector ranking model based on manifold learning are proposed. Finally, based on the above research, this paper builds a platform performance evaluation prototype system that integrates BSC model to help platform managers effectively identify the key performance factors in the system, take effective measures to optimize and improve, and improve the research and development and translation platform to gather resources and transform technology efficiency.

运行绩效的提升是研发与转化功能性平台有效聚集资源和转化科技的关键因素。本项目针对平衡计分卡（BSC）体系在绩效管理实践中所面临的彼此相关的四个重要问题加以研究。首先，考虑平台运行效率影响因素的多维性，利用文献分析和问卷调查的方法构建绩效影响因素BSC体系，并集成BSC和DEMATEL方法分析其因果关联。其次，考虑绩效指标权重的复杂性，提出基于FANP重要性权重和基于离差最大化法分类权重的组合权重确定方法。第三，由于绩效评价的不确定性和多样性，提出基于区间二元语言变量和混合加权平均算子的评价信息获取策略。第四，考虑指标信息的未确知性，提出融合流形学习的未确知测度综合评价模型。最后，在上述研究的基础上建立综合BSC模型的平台绩效评价原型系统，从而帮助平台管理人员有效识别系统中关键绩效因子，采取有效优化改进措施，提高研发与转化功能性平台聚集资源和转化科技的效率。

设置有效的高技术产业研发与转化功能性平台运营模式是当前高技术产业内商业生态系统和谐建设以及整体竞争力提升中的迫切需要解决的深层次课题，也是政府引导培育高技术产业的重要抓手。本项目基于平衡计分卡（BSC）对研发与转化平台绩效管理实践中所面临的彼此相关的几个重要问题开展了以下研究：(1)通过研究平台运行效率影响因子的多维性，构建了绩效因子层次框架模型及其关联关系分析模型，并建立了一种主观客观权重相结合的效率影响因子权重计算方法，解决了实践中绩效因子权重信息不完全性的问题；(2) 通过研究平台运行效率评价的不确定性和多样性，设计了一种不确定评价信息获取策略，减少了不合理评价对绩效水平分析的影响，防止了语言评估信息在处理过程中的扭曲和丢失问题；(3)考虑指标信息的未确知性，构建了—种不需要—致性检验就能够对非—致性判断矩阵进行排序的模型，确保了所获取的数据的真实和相关分析更为准确。(4)最后开发了一个集成BSC模型的平台绩效评价原型系统，并推广应用到研发与转化平台的实际绩效评价管理中，有效提高了 研发与转化功能性平台聚集资源和转化科技的效率。本项目完成了预定的主要研究内容，取得了超过预期的研究成果。在Argumenta Oeconomica、Chinese management studies、运筹与管理、同济大学学报(自然科学版)等国内外知名学术期刊上累计发表论文10篇，参与国际知名会议INFORMS 1次，出版中文专著1部，该专著获得上海汽车工业教育基金会出版资助奖。项目执行期间，培养硕士研究生21名，邀请2名国内外学者来校合作交流，项目组成员出国合作交流3人次。此外 ，通过本项目的研究积累，项目负责人入选国家留学基金委CSC访问学者，前往剑桥IFM研究院交流一年，聚焦创新管理和绿色生态系统相关领域的研究。

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