In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided.

A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016

A matter of life and death.

The literature of data envelopment analysis (DEA) encompasses many surveys, yet all either emphasize methodologies or do not make a distinction between methodological and application papers. This study is the first literature survey that focuses on DEA applications, covering DEA papers published in journals indexed by the Web of Science database from 1978 through August 2010. The results show that on the whole around two-thirds (63.6%) of DEA papers embed empirical data, while the remaining one-third are purely-methodological. Purely-methodological articles dominated the first 20 years of DEA development, but the accumulated number of application-embedded papers caught up to purely-methodological papers in 1999. Among the multifaceted applications, the top-five industries addressed are: banking, health care, agriculture and farm, transportation, and education. The applications that have the highest growth momentum recently are energy and environment as well as finance. In addition to the basic statistics, we uncover the development trajectory in each application area through the main path analysis. An observation from these works suggests that the two-step contextual analysis and network DEA are the recent trends across applications and that the two-step contextual analysis is the prevailing approach.

A survey of DEA applications

Cengage Learning, 2000. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary:

Applied Nonparametric Statistics

We propose a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach. We have previously published the network SBM (NSBM) and the dynamic SBM (DSBM) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of network structure within each period and, horizontally, we combine the network structure by means of carry-over activities between two succeeding periods. This model can evaluate (1) the overall efficiency over the entire observed period, (2) dynamic change of period efficiency and (3) dynamic change of divisional efficiency. The model can be implemented in input-, output- or non-(both) oriented forms under the CRS or VRS assumptions on the production possibility set. Finally, we applied this model to a dataset of US electric utilities and compared the result with that of DSBM.

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Dynamic DEA with network structure: A slacks-based measure approach

Two-stage network processes with shared resources and resources recovered from undesirable outputs

Allocating the fixed cost as a complement of other cost inputs: A DEA approach

As environment constraints on economic growth are strengthening, Carbon Emissions Abatement (CEA) allocation becomes a significant issue that draws academia׳s attention. In the literature, the Data Envelopment Analysis (DEA) technique has been applied to obtain CEA allocation with centralized models. Nevertheless, a centralized allocation plan suffers from an implementation difficulty in persuading decision-making units (DMUs) into an agreement. In this paper, we propose a new two-step method to mitigate this side effect. In the first step, we provide improved DEA-based centralized allocation models under the assumptions of constant returns-to-scale (CRS) and variable returns-to-scale (VRS) respectively and in the second step, two compensation schemes are developed for centralized allocation plans. An empirical application to the countries in Organization for Economic Co-operation and Development (OECD) is presented to elaborate the main idea.

Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA

This paper uses the Data Envelopment Analysis (DEA) technique to solve the problem of allocating a fixed cost across a set of comparable decision making units (DMUs) in a fair way. It first investigates the effect of the fixed cost on each DMU and on the collection of DMUs. Next we prove that there exist some cost allocations which can make each DMU and the collection of DMUs efficient. We show that such a cost allocation is unique and equivalent to the proportional sharing method if the fixed cost allocation problem is a one-dimensional case. In a multidimensional case, the fixed cost allocations may not be unique. This paper defines the concept of satisfaction degree, and proposes a maxmin model and a corresponding algorithm to generate a unique fixed cost allocation. Finally, the proposed approach has been applied to a data set from prior literature.

Allocating a fixed cost based on data envelopment analysis and satisfaction degree

In recent years, performance evaluation of sustainable operations has attracted a significant amount of research. The current paper seeks to evaluate sustainable manufacturing performance when wastes are recycled and re-used. For example, a typical sustainable process in iron and steel making involves in the first stage forging iron and steel products and in the second stage disposing the wastes. The sustainable manufacturing process then re-uses the recycled waste water in the first stage production. It is important that performance metrics include the operations related to recycling and re-use of the wastes. In a simplified view, such a sustainable operation is a two-stage process. Due to the ability of incorporating multiple performance metrics, data envelopment analysis (DEA) is used to develop our evaluation approach. While traditional DEA treats such two-stage manufacturing process as a black box by ignoring the internal conflicts and cooperation among multiple manufacturing stages, this paper proposes a modified two-stage network DEA model that deals with good and undesirable outputs from the sustainable manufacturing process. A Nash bargaining game between efficiencies of two-stages is proposed to produce unique and fair efficiency decomposition for the two-stage sustainable manufacturing process. The modified two-stage network DEA model is applied to wastewater recycling and reusing in a set of iron and steel makers in China. The results show that the proposed model is more effective than the black box DEA model in calculating efficiency of both two-stages and in identifying the sources of the inefficiency of overall sustainable manufacturing processes.

Liang Liang

Papers

Two-stage network processes with shared resources and resources recovered from undesirable outputs

Allocating the fixed cost as a complement of other cost inputs: A DEA approach

Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA

Allocating a fixed cost based on data envelopment analysis and satisfaction degree

Measuring performance of sustainable manufacturing with recyclable wastes: A case from China’s iron and steel industry