基于CAR-DEA方法的环境效率评价研究

现有环境效率评价的DEA方法没有考虑多维偏好约束问题,即不同决策单元对不同期望产出和不期望产出的偏好不同. 以地区为例,不同地区对GDP、废水和废气赋予的权重偏好各不相同. 在这种情况下,由于各决策单元的偏好约束不同,形成多维偏好约束集,在传统DEA模型中容易出现无可行解现象. 针对这一问题,基于CAR-DEA方法,结合保证域理论,提出一种解决多维偏好约束集问题的环境效率评价模型. 采用中国工业系统的环境效率评价实例对提出的方法进行了分析和说明.     

重心法去模糊的环境舒适性指标评价

环境舒适性向来都是比较模糊的概念,对于人体来说只是感官上的瞬间感觉,难以用精确的数学描述,而且主观性较强.考虑到常规的去模糊评价方法容易忽略较小权重因素的影响,导致信息"丢失"的情况,从环境舒适性角度出发,提出重心法去模糊的综合评价方法.方法引用力学中重心的概念,刻画各个环境因素隶属度的集中程度,解决了去模糊过程中出现结果模糊的问题.分析实施例表明,重心法去模糊综合评价方法的分析结果与调查的实际情况较为吻合,吻合度约为91.4%,说明评价方法更具合理性和有效性,为后续的环境舒适性控制器提供准确的输入参量.     

基于最近目标交叉视角下的环境效率评估方法北大核心CSCD

数据包络分析方法由于其非参性和客观性而被广泛应用于环境效率评估.基于最远目标数据包络分析通过最大化松弛变量来确定改进目标,在进行环境效率评估时存在面临巨大改进难度和高昂改进费用的缺陷.现有的基于最近目标的数据包络分析虽然有助于低效率决策单元以更小的变化达到高效,但大多是从自评角度出发,存在效率虚高的问题.针对这个问题,文章首先结合RAM模型,在考虑环境效率测度特点的基础之上,提出考虑非期望产出的最远目标RAM模型和最近目标RAM模型;然后指出此类模型存在的缺陷;在此基础之上,结合非径向方向距离函数,提出考虑环保性原则的最近目标环境交叉测度模型,从最近目标交叉视角出发测算各地区的环境效率,并利用熵权进行效率集结,使评估结果更具可靠性.最后,以福建省9个地市的环境效率为例,通过与不同环境效率测算方法对比,验证该方法具有较强的有效性和实用性.     

多指标面板数据因子分析的分层模型及应用

针对多指标面板数据的公因子提取及评价问题,提出一种充分挖掘面板数据时间序列价值的分层因子模型.模型在底层上通过对各个时点上截面数据指标变量的精炼实现对截面样本数据的评价,将截面数据压缩成只具有时间维度的样本评价值向量;模型顶层进一步实现了对由各个截面样本评价值向量形成的综合评价矩阵时间维度的精炼,并推导出面板数据因子得分公式及评价函数.最后,运用模型方法对我国大陆31个省市国有及规模以上非国有企业生产及经营状态面板数据进行了因子分析,分析结果显示了方法的合理性.分层模型实现了对面板数据各样本的指标维度与时间维度的双重提炼,弥补了现有方法的片面性与局限性.     

基于灵敏度分析的职业环境健康综合评价方法

职业环境综合评价是一种动态的评估模型,而传统的评价方法只能得到静态的固定评级或结果值,无法反映出系统的稳定性和评价的真实性.而灵敏度分析法可以反映出评价体系内各个指标的变化对最终评价结果的影响程度,即关联性,并且其适用与实际中的动态评价体系.针对传统方法中存在的缺点,提出基于灵敏度分析的职业健康综合评价方法:首先,利用熵权系数法计算出评价体系内各个指标的权重,其次,根据指标权重灵敏度分析,分别计算出各个指标的权重阈值变化范围.结果表明,该评价方法能够准确实际地反映出真实职业环境的评价结果,并且可以明显地观察到各个指标的变化阈值大小,为后续的政策制定和环境管理办法的提出,提供了有力的支持和帮助,适合在公共卫生领域实施和推广.     

基于直觉模糊AWD方法的供应商选择

供应商选择是供应链管理的关键环节之一.运用直觉模糊精确加权(AWD)和复杂比例评价(COPRAS)方法对供应商选择模型进行了研究,构建了基于直觉模糊精确加权的供应商选择的COPRAS模型.模型将供应商评价值的语言变量转化为直觉模糊数,运用直觉模糊的精确加权方法分别确定各个评价指标的精确权重和同一指标下各个供应商的精确权重,极大地提高了指标权重确定的科学性,然后结合COPRAS方法选择供应商.最后将构建的供应商选择模型在实例中进行了应用,并结合各供应商效用度得出了各个供应商的优先顺序,获得了最佳供应商选择方案,验证了所提出方法的可行性和有效性.     

英语标准化考试评价中IRT与CTT的比较研究

考试质量的评价对教学管理决策至关重要.随着大规模标准化考试的不断涌现,对公正考试和科学评判的要求越来越高,阐述了IRT理论的原理及估算方法,并通过实证研究将IRT与经典测试理论(CTT)进行了理论比较,并基于中科院研究生院英语标准化考试的数据,从试题评价参数估计和考生能力估计等方面进行了实证对比.结果表明:IRT对项目参数的估计不依赖样本变化而变化,比CTT具有明显的稳定性;同时,IRT记分充分考虑了各个项目的性能差异,被试的得分更少雷同,更为精确.     

考虑非期望因素的决策单元排序:两阶段综合效率法

对所有平行级别上的同等类型的决策单元(DMUs)在绩效表现上的排序一直是管理决策领域研究的重要课题之一。基于数据包络分析的超效率理论和SBM模型,探讨考虑非期望因素的DMUs排序问题。首先构建新的考虑非期望因素的超效率SBM模型,此模型不仅能对有效DMUs排序,而且能够转化成线性规划问题求解,具有有界性、单调性等良好性质。然后将新模型与Tone的SBM模型结合提出了考虑非期望因素的SBM综合排序法,同时给出了相对应的多项式时间算法。该方法以SBM模型作为第一阶段完成非有效DMUs排序,以新模型作为第二阶段完成有效DMUs排序,两阶段综合即完成所有DMUs排序。研究结果表明,综合排序法能够完成对考虑非期望因素的DMUs的排序,为绩效评价的管理实践提供了重要的理论依据。选取中国2010年的30个省份为实证研究对象,应用所提出的综合效率排序法对其环境效率进行排序。分析结果与中国的现实情况的相吻合,表明该排序方法是合理的,能够完成对这些地区的环境效率进行排序,可以为决策者评价环境的绩效表现提供有效的决策支持。     

大数据挖掘用于电力企业评价体系构建

随着大数据时代的来临,电力企业需要探索新的管理模式.从数据出发,对大量历史监测指标数据进行统计分析,从中筛选出影响企业发展的关键指标,提取关键指标的历史特征.采用层次聚类和Kmeans聚类两种聚类方法对关键指标进行聚类分析,并且结合管理经验,与专家讨论对指标类簇进行合理的命名,最终将关键指标分为了服务电网、运营指数、风险指数三个能够反映公司管理程度的一级指数,并且在一级指数下又分为了若干个子指数,建立了适合新源控股公司管理发展的评价体系.与传统的公司评价体系构建的方法相比,基于大数据挖掘方法构建的评价体系更能够体现出各个指标之间的内在联系,从而能够更好的为决策者提供管理建议.     

基于ANP-Fuzzy模型的农产品供应链风险评价研究

供应链风险大小直接影响供应链运作的效率和效益.现有的农产品供应链风险评价研究文献中对农产品供应链风险的系统度量研究不多.提出了农产品供应链风险评价体系,从种植、组织、流通、加工、供应、需求、环境七个方面构建了农产品供应链风险评价指标体系,运用ANP法确定指标权重,构建了基于ANP-Fuzzy模型的农产品供应.链风险评价方法,在此基础上,对农产品供应链风险进行实证分析,说明模型的实用性.     

Solving the knapsack problem with imprecise weight coefficients using genetic algorithms

This paper investigates solving the knapsack problem with imprecise weight coefficients using genetic algorithms. This work is based on the assumption that each weight coefficient is imprecise due to decimal truncation or coefficient rough estimation by the decision-maker. To deal with this kind of imprecise data, fuzzy sets provide a powerful tool to model and solve this problem. We investigate the possibility of using genetic algorithms in solving the fuzzy knapsack problem without defining membership functions for each imprecise weight coefficient. The proposed approach simulates a fuzzy number by distributing it into some partition points. We use genetic algorithms to evolve the values in each partition point so that the final values represent the membership grade of a fuzzy number. The empirical results show that the proposed approach can obtain very good solutions within the given bound of each imprecise weight coefficient than the fuzzy knapsack approach. The fuzzy genetic algorithm concept approach is different, but gives better results than the traditional fuzzy approach.     

Scale directional distance function and its application to the measurement of eco-efficiency in the manufacturing sector

Directional distance function (DDF) is a recognized technique for measuring efficiency while incorporating undesirable outputs. This approach allows for desirable outputs to be expanded while undesirable outputs are contracted simultaneously. A drawback of the DDF approach is that the direction vector to the production boundary is fixed arbitrarily, which may not provide the best efficiency measure. Therefore, this study extends the previous framework of efficiency analysis to introduce a new slacks-based measure of efficiency called the scale directional distance function (SDDF) approach. This new approach determines the optimal direction to the frontier for each unit of analysis and provides dissimilar expansion and contraction factors to achieve a more reasonable eco-efficiency score. This new approach is employed to measure the eco-efficiency of the Malaysian manufacturing sector. In addition, the paper demonstrates the use of the new approach to establish target values for the reduction/expansion of outputs in order for the inefficient DMUs to achieve full eco-efficiency. The results indicate that Melaka, Pulau Pinang, Negeri Sembilan, Sabah, Sarawak and Labuan have attained full eco-efficiency while Terengganu is the least eco-efficient. The overall eco-efficiency of the manufacturing sector in Malaysia is 80.5 % with wide variations across the states.     

Eco-efficiency and eco-productivity change over time in a multisectoral economic system

We measure eco-efficiency of an economy by means of an augmented Leontief input–output model extended by constraints for primary inputs. Using a multi-objective optimisation model the eco-efficiency frontier of the economy is generated. The results of these multi-objective optimisation problems define eco-efficient virtual decision making units (DMUs). The eco-efficiency is obtained as a solution of a data envelopment analysis (DEA) model with virtual DMUs defining the potential and a DMU describing the actual performance of the economy. This procedure is then extended to an intertemporal approach in the spirit of the Luenberger productivity indicator. This indicator permits decomposing eco-productivity change into eco-efficiency change and eco-technical change. The indicator is then further decompounded in a way that enables us to examine the contributions of individual production factors, undesirable as well as desirable outputs to eco-productivity change over time. For illustration purposes the proposed model is applied to investigate eco-productivity growth of the Austrian economy.     

基于DEA的污染物排放配额分配方法研究

文章首先提出一种典型的通过分配污染物排放配额改善环境状况的环境管理问题,在分析问题特性的基础上,提出一种基于DEA的污染物排放配额的分配方法,该方法将污染物排放配额作为一种决策变量,在求解系统整体效率的同时得到各决策单元的配额分配量。然后采用淮河流域造纸厂的实例说明了该方法的合理性和可行性。由于本文提出的方法考虑环境管理实际情况,在分配配额时能有效提高整个系统的环境效率,能为环境管理政策的制定提供有效的参考信息,具有很大的应用价值.     

A framework for analysing decisions under risk

The main objective is to present a framework for analysing decisions under risk. The nature of much information available to decision makers is vague and imprecise, be it information for human managers in organisations or for process agents in a distributed computer environment. Some approaches address the problem of uncertainty, but many of them concentrate more on representation and less on evaluation. The emphasis in this paper is on evaluation and even though the representation used is that of probability theory, other well-established formalisms can be used. The approach allows the decision maker to be as deliberately imprecise as he feels is natural and provides him with the means for expressing varying degrees of imprecision in the input sentences. The framework we present is intended to be tolerant and to provide means for evaluating decision situations using several decision rules beside the conventional maximisation of the expected utility.     

Data envelopment scenario analysis with imprecise data

In the existing DEA models, we have a centralized decision maker (DM) who supervises all the operating units. In this paper, we solve a problem in which the centralized DM encounters limited or constant resources for total inputs or total outputs. We establish a DEA target model that solves and deals with such a situation. In our model, we consider the decrease of total input consumption and the increase of total output production; however, in the existing DEA models, total output production is guaranteed not to decrease. Considering the importance of imprecise data in organizations, we define our model so as to deal with interval and ordinal data. A numerical illustration is provided to show the application of our model and the advantages of our approach over the previous one.     

Posynomial Parametric Geometric Programming with Interval Valued Coefficient

The article presents solution procedure of geometric programming with imprecise coefficients. We have considered problems with imprecise data as a form of an interval in nature. Many authors have solved the imprecise problem by geometric programming technique in a different way. In this paper, we introduce parametric functional form of an interval number and then solve the problem by geometric programming technique. The advantage of the present approach is that we get optimal solution of the objective function directly without solving equivalent transformed problems. Numerical examples are presented to support of the proposed approach.     

Integrating expert weighting and multi-criteria decision making into eco-efficiency analysis: the case of US manufacturing

In this paper, the effect of weighting strategies on sustainability performance assessment is addressed. Eco-efficiency is used as the main metric for sustainability performance evaluation. An integrated input-output life cycle assessment (LCA) and multi criteria decision making (MCDM) approach is employed. The US manufacturing sectors’ LCA results are used in conjunction with the proposed MCDM framework to perform the eco-efficiency evaluation of 276 US manufacturing sectors. Five environmental impact categories are considered as the negative factors, namely: greenhouse gas emissions, energy use, water withdrawal, hazardous waste generation and toxic releases into air and the economic output of each manufacturing sector is considered to be the positive output. To study the overall impact of different weighting strategies; twenty weighting scenarios are designed. Five pairs of weights considered for the overall economic versus environmental impacts along with four specific weighting strategies based on Harvard, SAB, EPP and Equal weighting for each pair. According to the results of the statistical analysis, it is concluded that the weighing strategies applied to the overall environmental impacts and economic outputs cause statistically significant differences in the eco-efficiency scores.