油料供应标准制定中信息真实性问题探析

提出了油料供应标准制定中信息真实性问题,建立了信号示意模型,并对模型进行了分析,得出了解决油料供应标准制定中信息真实性问题的结论,为油料供应标准的合理制定提供理论基础。     

军队油料供应标准制定的博弈分析

通过对总部和部队在军队油料供应标准制定中博弈行为的描述，运用不完全信息讨价还价模型讨论了军队油料供应管理标准制定的博弈策略。求解模型并对结果进行分析．最后得出总部的最优策略。     

军用油料供应安全影响因素分析与控制研究

界定了军用油料供应安全概念,划分了安全类别,分析了影响油料供应安全的因素,并针对油料供应的不同环节,提出了油料供应安全控制的方法。     

军用油料供应安全影响因素分析与控制研究

界定了军用油料供应安全概念,划分了安全类别,分析了影响油料供应安全的因素,并针对油料供应的不同环节,提出了油料供应安全控制的方法.     

基于三角模糊AHP和超效率DEA的物流系统评价

传统的DEA、AHP或者二者的单纯结合在评价物流绩效时还存在着一些不足。文章提出了基于三角模糊AHP和超效率DEA的物流系统评价法,考虑经验的模糊性的同时结合了超效率DEA,更全面、更实际地评价了物流系统。     

绿色供应链模式下的供应商选择

首先介绍了绿色供应链的内涵,然后在传统供应链供应商选择理论的基础上,根据环境管理标准（ISO14001系列）,经过扩展而建立了一种新的基于绿色供应链的供应商选择评价指标体系,将绿色信息作为绿色供应商评价的主要衡量指标之一;并结合传统数据包络分析法（DEA）和层次分析法AHP算法的优点,根据指标体系的特点采用DEA／AHP综合评价法．最后给出一个算例进行说明,为绿色供应商的决策提供了理论依据。     

供应链竞争力综合评价的AHP/DEA模型

企业供应链竞争力评价问题是供应链管理中一个极其重要而复杂的问题。在构建企业供应链竞争力的评价指标体系基础上,依据层次分析法(AHP)和数据包络法(DEA)各自具有的特点,构造了基于两者结合的AHP/DEA综合评价模型对供应链竞争力进行综合评价,AHP/DEA综合评价模型能充分兼顾考虑主客观因素。实例应用表明,AHP/DEA模型具有有效性和科学性。     

基于AHP-EWAA和BP神经网络的油料调拨绩效评价模型研究

针对当前油料调拨绩效评价不足的现状和评价意义的重要性,根据油料调拨工作的实际,提出了油料调拨绩效评价指标体系的建立原则与方法,并建立了系统、合理的评价指标体系,运用AHP法确定了各指标因素的相应权重,并建立了基于EWAA算子和BP神经网络的评价模型,进行了实例应用,验证了两种模型方法的可用性。     

第三方物流服务的时效性研究

本文就企业业绩满意度和需方满意度两方面分别建立了第三方物流服务时效性的评价指标体系,对相关指标给出了一套科学有效的定量方法。给出了基于DEA法的第三方物流服务时效性评价标准,并对弱时效性的物流企业利用投影分析给出了其改进方向和幅度。     

将第三方物流思想引入军队油料联勤改革之中

在长期的军队油料供应保障中,油料物流按照建制隶属关系形成了自成体系、自我保障的传统物流体制,与现行油料联勤保障改革产生了一定的利益冲突。本文试图将第三方物流思想引入军队油料保障之中,以推动油料联勤改革的进一步完善。     

基于DEA的区域高等院校协作效率评价

本文应用DEA方法从定量的角度研究通过区域内和区域之间的合作带来的益处。通过构建评价高等院校协作效率的DEA模型,对某地14所院校的区域内和区域间的高等院校协作效率进行评价。计算结果表明,通过区域内合作,每一个区域内都有部分院校的效率有较大的提高,最高的超过30％。     

绩效标杆模型在输配电价管制中的应用

管制者在制定输配电价格时,常采用基于RPI-X公式的价格上限、收益上限、与标尺竞争管制,对于效率因数X的确定,许多国家倾向于应用基于企业的相对效率制定绩效标杆来确定。前沿绩效标杆方法主要运用DEA求出绩效较好的企业,作为参照绩效。本文讨论DEA方法在绩效标杆法中的应用,并用绩效标杆法解决电价管制的问题。     

运输型物流企业生产效率评价

文中基于我国18家运输型物流上市企业2012年财务报表数据,运用数据包络分析方法对这些运输型物流企业的生产效率进行测度。结果发现,我国运输型物流企业的生产效率在管理费用,固定资产等方面存在较严重的冗余现象。大部分运输型物流企业处于规模效益递减阶段,应通过控制管理费用、充分利用劳动力、降低运营成本、改善规模效益递减状况的方式,实现生产效率的提升。     

Localized Innovation and Productivity Growth in Asia: An Intertemporal DEA Approach

Recent contributions to growth theory stress the importance of localized innovation for the performance of more backward countries. In earlier papers, analyses by means of DEA techniques confirmed this intuition. In this paper, we extend this type of analysis by relaxing the macroeconomic viewpoint adopted until now. New databases on output, labor and capital input in the agricultural and manufacturing sectors are developed for 40 countries. Using intertemporal DEA, it is found that changes in the global production frontier are localized at high levels of capital intensity. This result is stronger in agriculture than in manufacturing. Further, a decomposition of labor productivity growth in eight Asian countries for the period 1975–1992 into the effects of capital intensification, learning and innovation is made. The results suggest that there is a particular development path in which increases in capital intensity appear to be a prerequisite to benefit from international technology spillovers.JEL Classification: O14, O30, O40, O47     

Measuring efficiency under fixed proportion technologies

Data Envelopment Analysis (DEA) applications frequently involve nonsubstitutable inputs and nonsubstitutable outputs (that is, fixed proportion technologies). However, DEA theory requires substitutability. In this paper, we illustrate the consequences of nonsubstitutability on DEA efficiency estimates, and we develop new efficiency indicators that are similar to those of conventional DEA models except that they require nonsubstitutability. Then, using simulated and real-world datasets that encompass fixed proportion technologies, we compare DEA efficiency estimates with those of the new indicators. The examples demonstrate that DEA efficiency estimates are biased when inputs and outputs are nonsubstitutable. The degree of bias varies considerably among Decision Making Units, resulting in substantial differences in efficiency rankings between DEA and the new measures. And, over 90% of the units that DEA identifies as efficient are, in truth, not efficient. We conclude that when inputs and outputs are not substituted for either technological or other reasons, conventional DEA models should be replaced with models that account for nonsubstitutability.     

Resource Commitment,Organizational Diversity,and Research Performance: A Case of the National Telecommunication Program in Taiwan

Traditionally, a project team's resource commitment and organizational diversity are thought to be helpful to project performance; however, this relationship may falter when the project performance is considered in light of both cost and benefit. This paper integrates data envelopment analysis (DEA) and analytical hierarchy process (AHP) to evaluate project performance and explores the relationship between project performance and resource commitment and organizational diversity. The empirical study of research teams in the Taiwan National Telecommunication Program shows mixed results of this relationship. Implications for project management and governmental subsidy policy in dealing with resource commitment and organizational diversity are discussed.     

Incorporating quality into the measurement of hospital efficiency: a double DEA approach

This paper uses “double DEA” to assess how accounting for quality influences DEA technical efficiency scores of a sample of 1,074 US hospitals. In the first use of DEA, quality indices are estimated using a variety of process and outcome measures of quality. In the second use of DEA, technical efficiency is assessed while controlling for quality. A variety of DEA quality indices and a DEA variety of efficiency models are compared to determine how the treatment of quality influences findings regarding technical efficiency. Controlling for efficiency does matter, with outcome measures having an apparently greater impact than process measures. Given the call for improved quality and better cost containment, controlling for quality is an important contribution to efficiency analysis.     

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Data envelopment analysis (DEA) is in fact more than just being an instrument for measuring the relative efficiencies of a group of decision making units (DMU). DEA models are also means of expressing appreciative democratic voices of DMUs. This paper proposes a methodology for allocating premium points to a group of professors using three models sequentially: (1) a DEA model for appreciative academic self-evaluation, (2) a DEA model for appreciative academic cross-evaluation, and (3) a Non-DEA model for academic rating of professors for the purpose of premium allocations. The premium results, called DEA results, are then compared with the premium points “nurtured” by the Dean, called N bonus points. After comparing DEA results and N bonus points, the Dean reassessed his initial bonus points and provided new ones – called DEA-N decisions. The experience indicates that judgmental decisions (Dean's evaluations) can be enhanced by making use of formal models (DEA and Non-DEA models). Moreover, the appreciative and democratic voices of professors are virtually embedded in the DEA models.     

A PCA–DEA approach to measure the quality of life in Estonian counties

This paper investigates the application of a PCA–DEA model to assess the quality of life (QOL) scores in Estonian counties and analyses the model's results. The dataset is a balanced panel of 15 Estonian counties covering the period from 2000 to 2011. We consider a PCA–DEA model as an alternative method to estimate and predict QOL scores and rankings of Estonian counties. The method consists of a two-stage analysis that begins with a principal component analysis. In the second stage, the standard DEA is used. The results from the conventional DEA model and the PCA–DEA model are compared and discussed. A comparison of the methodologies demonstrates that a PCA–DEA model provides a powerful tool for performance ranking. The rankings of Estonian counties using QOL scores for different model specifications are presented. Finally, the QOL ranking of Estonian counties is revised using PCA–DEA.