Efficiency assessment of coal mine safety input by data envelopment analysis

In recent years improper allocation of safety input has prevailed in coal mines in China, which resulted in the frequent accidents in coal mining operation. A comprehensive assessment of the input efficiency of coal mine safety should lead to improved efficiency in the use of funds and management resources. This helps government and enterprise managers better understand how safety inputs are used and to optimize allocation of resources. Study on coal mine＇s efficiency assessment of safety input was con- ducted in this paper. A C^2R model with non-Archimedean infinitesimal vector based on output is established after consideration of the input characteristics and the model properties. An assessment of an operating mine was done using a specific set of input and output criteria. It is found that the safety input was efficient in 2002 and 2005 and was weakly efficient in 2003. However, the efficiency was relatively low in both 2001 and 2004. The safety input resources can be optimized and adjusted by means of projection theory. Such analysis shows that, on average in 2001 and 2004, 45% of the expended funds could have been saved. Likewise, 10% of the safety management and technical staff could have been eliminated and working hours devoted to safety could have been reduced by 12%. These conditions could have Riven the same results.

Robust data envelopment analysis based MCDM with the consideration of uncertain data

The application of data envelopment analysis （DEA） as a multiple criteria decision making （MCDM） technique has been gaining more and more attention in recent research. In the practice of applying DEA approach, the appearance of uncertainties on input and output data of decision making unit （DMU） might make the nominal solution infeasible and lead to the efficiency scores meaningless from practical view. This paper analyzes the impact of data uncertainty on the evaluation results of DEA, and proposes several robust DEA models based on the adaptation of recently developed robust optimization approaches, which would be immune against input and output data uncertainties. The robust DEA models developed are based on input-oriented and outputoriented CCR model, respectively, when the uncertainties appear in output data and input data separately. Furthermore, the robust DEA models could deal with random symmetric uncertainty and unknown-but-bounded uncertainty, in both of which the distributions of the random data entries are permitted to be unknown. The robust DEA models are implemented in a numerical example and the efficiency scores and rankings of these models are compared. The results indicate that the robust DEA approach could be a more reliable method for efficiency evaluation and ranking in MCDM problems.

Data envelopment analysis procedure with two non-homogeneous DMU groups

The classic data envelopment analysis（DEA） model is used to evaluate decision-making units＇（DMUs） efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output（IO） criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging（OWA） operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.

Prediction Model of Data Envelopment Analysis with Undesirable Outputs

Data envelopment analysis (DEA) has become a standard non parametric approach to productivity analysis, especially to relative efficiency analysis of decision making units (DMUs). Extended to the prediction field, it can solve the prediction problem with multiple inputs and outputs which can not be solved easily by the regression analysis method.But the traditional DEA models can not solve the problem with undesirable outputs,so in this paper the inherent relationship between goal programming and the DEA method based on the relationship between multiple goal programming and goal programming is explored,and a mixed DEA model which can make all factors of inputs and undesirable outputs decrease in different proportions is built.And at the same time,all the factors of desirable outputs increase in different proportions.

AIDS Treatments Efficiency Analysis Based on Cost Efficiency Data Envelopment Analysis Model

Three data envelopment analysis (DEA) models were used to analyse the relative efficiencies of four AIDS treatments in AIDS Clinical Trial Group (ACTG) Study 193A(1 309 patients in total, classified into 4 age groups). Results from the output-oriented BCC model show that Treatment 4 ( 600 mg of zidovudine plus 400 mg of didanosine plus 400 mg of nevirapine) is particularly efficient for age group 14—25, but not efficient for the older age groups; Treatment 1 (600 mg of zidovudine alternating monthly with 400 mg of didanosine)and Treatment 2 (600 mg of zidovudine plus 2.25 mg of zalcitabine) are efficient for the age groups 35—45 and 45— ; age group 25—35 does not have a particularly efficient treatment, but Treatments 1 and 2 are relatively good. The cost efficiency BCC model, which takes the treatment cost into account, gives similar results as the output-oriented model. Results from the indirect output-oriented BCC model, which allows the replacement among medicines, show that the efficiency of Treatment 2 has greatly decreased compared with that of the output-oriented model, and a set of optimal medicine amounts for different age groups is obtained.

A New Approach for Ranking Efficient Units in Data Envelopment Analysis and Application to a Sample of Vietnamese Agricultural Bank Branches

This paper proposes a new approach for ranking efficiency units in data envelopment analysis as a modification of the super-efficiency models developed by Tone [1]. The new approach based on slacks-based measure of efficiency (SBM) for dealing with objective function used to classify all of the decision-making units allows the ranking of all inefficient DMUs and overcomes the disadvantages of infeasibility. This method also is applied to rank super-efficient scores for the sample of 145 agricultural bank branches in Viet Nam during 2007-2010. We then compare the estimated results from the new SCI model and the exsisting SBM model by using some statistical tests.

The Treatment of Non-zero Slacks in Data Envelopment Analysis

The paper studies the non-zero slacks in data envelopment analysis. A procedure is developed for the treatment of non-zero slacks. DEA projections can be done just in one step.

Assessment of Dry Port Efficiency in Africa Using Data Envelopment Analysis

This work involves the evaluation of dry port competitiveness through analysis of efficiencies for selected dry ports in Africa. Five dry ports were selected and analysis carried out over a period of four years. The dry ports considered were Mojo and Kality in Ethiopia, Mombasa in Kenya, Isaka in Tanzania and Casablanca in Casablanca, Morocco. Data Envelopment Analysis (DEA) was applied for this work. Container throughputs for the various ports under consideration were used as the output variable for the data analysis model, while the number of reach stackers, the number of tractors, the number of forklifts and the size of the dry port were used as the input variables. From the results, the Mombasa dry port was found to be the most efficient with an average score of approximately 1 over the period under consideration. Casablanca was the second efficient dry port with an average score of 0.762, while Isaka was the least efficient with an average score of 0.142. This research is significant since the African countries have embraced the dry port concept, as witnessed in the huge investments in this sector, and would serve to highlight areas that need improvement for the few existing dry port facilities, most of which are undergoing expansion as well as modernization.

Public and Private Investment in the Port Sector in Mexico, 2000-2010： A Study Through Data Envelopment Analysis

Knowing the level of efficiency of investment applied in ports of Mexico is relevant information for the design of port policies that contribute to its development and thus to greater freight movement. The objective of this paper is to analyze the technical efficiency obtained from International Mexican Ports, through the use of the technique of Data Envelopment Analysis （DEA）. It uses data regarding public and private investment in ports applied during the period 2000-2010 and its influence on the number of Twenty-foot Equivalent Unit （TEU）. Because it has been applied the DEA-CCR （the linear programming model） model input oriented, thus not only the efficiency is calculated in ports, but benchmarking is also obtained to determine the efficient ports that serve as reference to those who were found to be inefficient. The results obtained showed that Manzanillo and Progreso were the most efficient ports. On the other hand, the ports that were not efficient for any of the years reviewed were Mazatlan and Lazaro Cardenas. Generally, public investment has been increasing over the period, and public policies are not designed to allow the ports to have an international projection.

Evaluate the Investment Efficiency by Using Data Envelopment Analysis: The Case of China

Although investment is regarded as a key force of China’s economic growth, little study has been done to measure China’s investment efficiency. The present paper applies the data envelopment analysis (DEA) to Chinese provincial panel data from the year 2003 to 2008 for measuring the investment efficiencies and identifying their trends of Chinese 30 provinces and autonomous regions. A cross-efficient DEA model with considering benevolent formulation is used for providing accurate efficiency scores and completely ranking. The empirical results suggest that the differences of investment efficiency in different regions are distinct but tending to diminish year by year, and the investment efficiencies in some provinces are significantly correlated to their investment rates to the national total investment.

Data Envelopment Analysis Model for Assessment of Safety and Security of Intermodal Transportation Facilities

Following September 11, 2001, numerous security policies have been created which have caused a number of unique challenges in planning for transportation networks. Transportation policy and funding to improve the transportation infrastructure has historically been addressed as individual modes not as intermodal transportation. As a consequence of this inopportune allocation, it is now apparent that the transportation modes are disconnected and have unequal levels of security and efficiency. Improved intermodal connectivity has therefore been identified as one of the main challenges to achieve a safer, secure, and productive transportation network. Tools need to be refined for collaboration and consensus building to serve as catalysts for efficient transportation solutions. In this study, a mathematical model using data envelopment analysis (DEA) was developed and investigated to assess the safety and security of intermodal transportation facilities. The model identifies the best and worst performers by assessing several safety and security-related variables. The DEA model can assess the efficiency level of safety and security of intermodal facilities and identify potential solutions for improvement. The DEA methodology presented is general in its framework and can be applied to any network of intermodal transportation systems. Availability of credible data, complemented with DEA methodology will help in management decisions making concrete safety and security decisions for intermodal transportation facilities.

Failure Mode and Effects Analysis （FMEA） by Fuzzy Data Envelop Analysis （Fuzzy-DEA）

Failure mode and effects analysis （FMEA） offers a quick and easy way for identifying ranking-order for all failure modes in a system or a product. In FMEA the ranking methods is so called risk priority number （RPN）, which is a mathematical product of severity （S）, occurrence （0）, and detection （D）. One of major disadvantages of this ranking-order is that the failure mode with different combination of SODs may generate same RPN resulting in difficult decision-making. Another shortfall of FMEA is lacking of discerning contribution factors, which lead to insufficient information about scaling of improving effort. Through data envelopment analysis （DEA） technique and its extension, the proposed approach evolves the current rankings for failure modes by exclusively investigating SOD in lieu of RPN and to furnish with improving sca.les for SOD. The purpose of present study is to propose a state-of-the-art new approach to enhance assessment capabilities of failure mode and effects analysis （FMEA）. The paper proposes a state-of-the-art new approach, robust, structured and useful in practice, for failure analysis.

Selecting the Six Sigma Project: A Multi Data Envelopment Analysis Unified Scoring Framework

The importance of the project selection phase in any six sigma initiative cannot be emphasized enough. The successfulness of the six sigma initiative is affected by successful project selection. Recently, Data Envelopment Analysis (DEA) has been proposed as a six sigma project selection tool. However, there exist a number of different DEA formulations which may affect the selection process and the wining project being selected. This work initially applies nine different DEA formulations to several case studies and concludes that different DEA formulations select different wining projects. Also in this work, a Multi-DEA Unified Scoring Framework is proposed to overcome this problem. This framework is applied to several case studies and proved to successfully select the six sigma project with the best performance. The framework is also successful in filtering out some of the projects that have “selective” excellent performance, i.e. projects with excellent performance in some of the DEA formulations and worse performance in others. It is also successful in selecting stable projects;these are projects that perform well in the majority of the DEA formulations, even if it has not been selected as a wining project by any of the DEA formulations.

Evaluation of Energy Efficiency and Analysis of Influencing Factors of Company CW’s Manufacturing Workshops

In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 12 Company CW’s manufacturing workshops from 2016 to 2022 was assessed.The findings indicated that aside from a few workshops operating at the production frontier,the rest exhibit significant fluctuations in energy efficiency and generally low energy efficiency.Subsequently,a combined GRA-Tobit analysis model was introduced to identify factors influencing the energy efficiency of Company CW’s manufacturing workshops.Regression analysis revealed that technological investments,employee quality,workshop production scale,investment in clean energy,and the level of pollution control all significantly impact the energy efficiency of Company CW’s manufacturing workshops.By evaluating the energy efficiency of Company CW’s manufacturing workshops and studying their influencing factors,this research aids company managers in understanding the energy efficiency of the manufacturing process.It optimizes the combination of various production elements,thereby offering effective guidance for improving the energy efficiency issues of the company’s manufacturing workshops,which can contribute to enhancing the corporation’s overall energy efficiency.

基于CH-DEA模型的中国制造业产业安全研究

2020年初,新冠疫情在世界范围内全面暴发,中国制造业受冲击显著,影响了中国整体经济健康稳定的发展。因此,分析中国制造业产业安全态势以应对制造业安全风险势在必行。创新地提出CH-DEA方法,并基于产业安全理论建立了制造业安全评价指标体系,从而科学合理地认识中国制造业产业安全现状。结果显示,2005~2020年期间中国制造业产业安全度总体呈现上升趋势,即制造业应对风险等突发事件能力及独立发展能力不断提高。微观障碍因素分析发现,现阶段中国制造业所面临的主要障碍是规模缩减,应利用科研创新能力提升制造业增加值。宏观环境影响因素分析显示,制造业产业安全与GDP、产品市场显著正相关。产品市场的良性竞争可以促使制造企业投入更多的资金采取规模化生产并更新设备,从而提升行业竞争力。

Empirical Analysis of China's Direct Food Subsidy Policy Based on DEA Model: A Case Study of Direct Food Subsidy Policy in Shandong Province

The direct food subsidy is an important agricultural subsidy policy under current conditions,more efficient than grain price protection policy and other policies,and it is of great importance to the agricultural development,but this policy will not meet the interests of society in the long run. In this paper,we make an empirical study with the direct food subsidy policy in Shandong Province as an example,focusing on the analysis of the efficiency of policy implementation in different regions. Study suggests that direct food subsidy has made some achievements,but there are some problems in practice,such as low standard for direct food subsidy policy,less subsidy varieties,generally low efficiency of direct food subsidy policy,lack of unified policy implementation,serious waste of money and inefficient supervision during the subsidy policy implementation process. In order to improve the efficiency of direct food subsidy,it is necessary to raise standard for subsidy,and expand the scope of subsidy varieties; unify the policy,and make the policy suit local circumstances; improve the subsidy mechanism,regulate government behavior,and strengthen supervision.

基于超效率DEA模型的陆军部队指挥所指挥效率评价

指挥所指挥效率评估是复杂的系统性问题,对提高部队指挥、作战、保障能力具有重要意义。主要运用数据包络的方法,不探讨指挥所的内部结构问题,通过对陆军典型部队指挥所输入输出数据进行分析,运用CCR-DEA和超效率DEA模型,对指挥效率问题进行研究排序,并针对输入数据提出改进建议,评估方法简单、可操作性强,对于陆军部队指挥所构建和指挥能力提升具有重要意义。

基于效用网络DEA的银行运营绩效评价

针对两阶段网络数据包络分析法(data envelopment analysis,DEA)中加法和乘法效率分解模型所存在的问题,文章提出期望效用理论分析框架下的效率评价方法。首先考虑子系统的内在关系,给出子系统期望效率的定义,并通过蒙特卡洛仿真算法进行求解;然后利用多属性效用函数和不含投入的DEA模型集成得到系统总效率;进而将该方法拓展到一般性的多阶段序列网络数据包络分析法结构中,扩大其应用范围;最后通过对中国上市商业银行运营效率的评价来验证方法的有效性与合理性。

基于双目标DEA模型的中国省级高技术产业的创新效率评价

科学合理地评价高技术产业的创新效率,对于提升其国际科技竞争力具有重要意义。基于创新价值链理论,高技术产业创新活动一般被划分为研发和商业化两个前后连续的子阶段。本文基于合作共赢的思想,从研发子阶段和商业化子阶段合作视角出发,建立双目标DEA模型。然后对中国高技术产业在2013—2015年(包括三个连续的创新周期,即2013年代表2013—2015年;2014年代表2014—2016年;2015年代表2015—2017年)的整体创新效率、研发效率和商业化效率进行评价。实证结果表明:中国高技术产业的整体创新效率偏低,具有较大的改进空间;高技术产业的研发效率远大于商业化效率,较低的商业化效率是导致整体创新效率偏低的主因;高技术产业的整体创新效率、研发效率和商业化效率存在省际和地区之间的差异。最后,根据研发效率和商业化效率,本文将29个省级高技术产业划分为四类,为每个省级高技术产业创新效率的提升提出具体策略。本文丰富了评价理论与方法,为高技术产业的创新效率评价提供了一个新的视角,具有重要的理论价值和实践意义。

基于DEA-Tobit模型的华东地区公共体育服务效率研究

公共体育服务是政府为满足公民体育需求所提供的一种体育服务措施,而效率评价是衡量其发展的重要手段。科学的公共体育服务效率评价,能够客观清晰地反映公共体育服务发展情况。构建DEA模型对2016—2018年我国华东地区六省一市的公共体育服务效率评价指标进行测算分析,并运用Tobit模型进行实证检验,探究可能对公共体育服务效率带来影响的外部因素。结果显示:(1)华东地区2016—2018年公共体育服务综合效率值为0.931,纯技术效率值为0.992,规模效率值为0.937,规模报酬呈现递减趋势;(2)地区经济发展水平、人口密度和体育管理人员规模与公共体育服务效率呈正相关,与受教育水平呈负相关。根据研究结果分析华东地区公共体育服务效率问题与差距,提出以下对策建议:减少区域短板,促进协同发展;调整资源配置,提升供给质量;健全监督机制,完善管理体系。