基于Malmquist的重庆市科研院所知识生产效率分析

利用中国31个省市自治区科研院所2009—2011年的数据,采用DEA（Data Envelopment Analysis,数据包络分析）和Malmquist指数分析方法从静态和动态两个角度对重庆市科研院所的知识产出效率进行比较分析,并利用DEA-Tobit两步法找出影响科研院所效率的主要影响因素。研究发现：重庆市科研院所的知识生产效率总体处于偏低状态,其科技资源的利用效率存在提升空间;重庆市科研院所未来改革的方向应集中在财政资助方式的变革,加大博士层级的科技人才培养力度,以及提高科技人才劳务支出等。最后提出了相应的政策建议。     

河南工业绿色发展效率演化及其影响因素研究

推动工业绿色发展,是中原经济区建设的核心任务之一。基于标杆管理思想,运用DEA模型对2001-2012年河南省工业绿色发展效率演化趋势进行研究,并通过灰色斜率关联度模型分析了工业绿色发展效率的影响因素。结果表明:河南省工业绿色发展效率呈现缓慢上升的演化趋势,工业外延式发展特征明显;究其原因,工业结构重型化和环境规制不足制约了工业绿色发展效率的提高,城市化、工业化、产业转移、经济发展水平这4个因素对河南省工业绿色发展效率有较大的正向促进作用,创新驱动因素对河南省工业绿色发展效率的提高仅有微弱的促进作用。最后,根据研究结果提出了促进河南省工业绿色发展的政策建议。     

两阶段链视角下河南省城市科技效率评价研究

建立两阶段链DEA模型,研究科技投入产出效率,由第一阶段科技创新和第二阶段科技应用组成。选取了2006~2013年共8年数据,对河南省城市科技投入产出进行评价分析,组成结果显示:(1)一些城市科技资源配置机制运行较差;(2)城市技术效率呈现以郑州为中心的偏高,其他城市较低,且其他城市与郑州市的科技效率差距较大。     

基于带偏好 DEA 模型的长株潭城市群公路交通效率评价

从城市群公路交通系统的输入和输出角度出发,给出城市群公路交通效率的输入输出指标体系,建立数据包络分析(data envelopment analysis,DEA)模型及带偏好DEA(A-prefer-DEA,PDEA)模型,进而对长株潭、长三角和珠三角城市群公路交通效率进行有效计算和综合评价.传统DEA模型的评价结果表明:长株潭城市群公路交通效率为DEA弱有效,长三角和珠三角公路交通效率为DEA有效;PDEA模型评价结果更直观,长三角、珠三角和长株潭公路交通效率值分别为1.678 2、1.577 9和0.866 2,长株潭公路交通效率与长三角和珠三角的差距比较明显.     

基于CI综合指数的DEA模型在备件ABC分类管理中的应用

ABC分类库存法是目前企业库存管理方面广泛应用的分类方法之一。将DEA模型结合CI综合指数法应用到了库存备件的分类管理中。通过这种方法与DEA-CCR方法的对比,得出综合指数法在库存备件的分类时效果更佳。同时,对DEA-CI模型中的非阿基米德值和λ值的选取给出了直观的解释和经验性的指导。     

水生态文明城市建设相对有效性分析

本文利用数据包络线法(DEA)分析水生态文明城市建设过程中,资源投入对于水生态环境改善的影响程度,通过对建设决策单元的有效性及投影分析,指导资源的投入,实现资源的优化配置,为水生态文明城市建设评价指标体系的构建以及综合评价提供建议。     

基于DEA模型的高校办学效率评价及应用

效率评价是高等院校面临的重要问题之一,建立数据包络分析(DEA)模型来研究高等院校效率评价具有独特的优势.首先在建立DEA模型的基础上,以29所"985高校"为例,在7个指标上对其科研方面的办学效率进行评价,分别测算了它们的技术效率、纯技术效率以及规模效率.其次根据规模收益分析,得到高校的发展趋势;根据投影分析,得到部分高校应做出的调整方向与力度.最后通过仿真比较得到各高校目前存在的问题并提出了相应的解决措施.     

An integrated approach for rating engineering characteristics’ final importance in product-service system development

Product-service system (PSS) approach has emerged as a competitive strategy to impel manufacturers to offer a set of product and services as a whole. A three-domain PSS conceptual design framework based on quality function deployment (QFD) is proposed in this research. QFD is a widely used design tool considering customer requirements (CRs). Since both product and services influence satisfaction of customer, they should be designed simultaneously. Identification of the critical parameters in these domains plays an important role. Engineering characteristics (ECs) in the functional domain include product-related ECs (P-ECs) and service-related ECs (S-ECs). ECs are identified by translating customer requirements (CRs) in the customer domain. Rating ECs’ importance has a great impact on achieving an optimal PSS planning. The rating problem should consider not only the requirements of customer, but also the requirements of manufacturer. From the requirements of customer, the analytic network process (ANP) approach is integrated in QFD to determine the initial importance weights of ECs considering the complex dependency relationships between and within CRs, P-ECs and S-ECs. In order to deal with the vagueness, uncertainty and diversity in decision-making, the fuzzy set theory and group decision-making technique are used in the super-matrix approach of ANP. From the requirements of manufacturer, the data envelopment analysis (DEA) approach is applied to adjust the initial weights of ECs taking into account business competition and implementation difficulty. A case study is carried out to demonstrate the effectiveness of the developed integrated approach for prioritizing ECs in PSS conceptual design.     

An enumeration algorithm for integer‐valued data envelopment analysis

Data envelopment analysis (DEA) models assume real‐valued inputs and outputs, but on many occasions, some inputs and/or outputs can only take integer values. In these cases, using DEA models can result in misleading efficiency assessments and inaccurate performance targets. In this paper, we propose an enumeration algorithm for computing efficiency scores and performance targets of decision‐making units with integer value inputs/outputs. In the presented algorithm, we do not use any of the mixed integer linear programming (MILP) models that are used in previous studies. We show that the result of our algorithm and that of the MILP model presented in this context is the same. We also generalize our algorithm for different types of returns to scale as well as for the hybrid setting with real‐valued data.