An application of data envelopment analysis in telephone officesevaluation with partial data

Using a Decision Analysis (DA) model for decision-making under partial probability information, we derive the basic Data Envelopment Analysis (DEA) models. The DA model has two components: the choice of a strategy and the occurrence of events. There is an outcome associated with each strategy–event combination. A Linear Programming (LP) model is formulated to reveal the strategies that are called efficient for given event probability ranges. Viewing the strategies as entities and the events as activity categories with outcomes that may have positive or negative effects on the entities' performance, the LP model can be configured for gauging the entities' productive performances. One particular configuration of the LP model called the LP-DM leads to the basic models of DEA. The LP-DM proffers a new starting point for developing methods that gauge productive performance and a different perspective that reiterates the caution that must be exercised in the application of DEA and use of its results for policy making.

Efficiency evaluation of homogenous decision making units (DMUs), is one of the primary objectives of data envelopment analysis (DEA), in which the large number of inputs or outputs compared with the number of DMUs leads to decreased evaluation precision of DEA models. Overcoming this problem is the main issue in two-level DEA. In the present study, we use the multi objective linear programming (MOLP) method proposed by Sumpsi et al., which is based on constructing the pay-off matrix and using goal programming (GP), to investigate this problem. Furthermore, we discuss the enhanced Russell's model for evaluating and benchmarking DMUs with two-level inputs and outputs. The proposed method is applied to 24 branches of an Iranian commercial bank.

Turbofan aero-engine efficiency evaluation: An integrated approach using VSBM two-stage network DEA

One of the major research streams in data envelopment analysis (DEA) is ranking decision-making units (DMUs). Utilizing a multicriteria decision-making technique, we develop a novel approach to fully rank all units. Motivated by the convex cone-based total order for multiple criteria alternatives proposed by Dehnokhalaji et al. (Nav Res Logist 61(2):155–163, 2014), we consider DMUs in DEA as multiple criteria alternatives and obtain their total ordering. Initially, some pairwise preference information is provided by the decision maker for units and the concepts of convex cones and polyhedral sets are defined in a DEA framework, correspondingly. We apply a modification of Dehnokhalaji et al. method to extract additional preference information for each pair of units and consequently obtain a full ranking (strict total ordering) of DMUs. The benefit of our approach to their method is that we apply non-radial models to overcome the instability drawback of radial models and their infeasibility occurring in DEA applications. The proposed approach is implemented for two numerical examples, and the accuracy of it is investigated through a computational test.

PurposePolice organizations' main goal is providing the security of the neighborhoods for citizens. This paper aims to investigate how efficiently the police stations in the city of Ankara work. It also seeks to determine how the efficiency of police organizations can be measured by using data envelopment analysis (DEA).Design/methodology/approachThe study uses DEA to measure the efficiency of police stations in the city of Ankara in Turkey. DEA measures the relative efficiency of decision‐making units (DMUs) based on their inputs and outputs.FindingsThe results showed that ten of 19 police stations in Ankara were efficient. In other words, 52.6 percent of police stations were found to be efficient.Research limitations/implicationsAll police stations in Ankara could not be included in the study. Since DEA analysis the relative efficiency of DMUs, including all police stations in Ankara, may have given a broader perspective for police managers.Practical implicationsInefficient police stations in this study should either reduce the number of incidents that occurred in the precinct or solve those crimes and increase the number of solved incidents. Preventing crimes may be achieved by employing community policing strategies, thus, police managers should focus on adopting community policing strategies.Originality/valueThe results of the DEA analysis can be used to help police stations in Ankara to be more efficient. Furthermore, applying DEA may be a new approach to measuring the efficiency of police services.

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

Free disposal hull efficiency scores of units with network structures

Conventional applications of data envelopment analysis generally treat the Decision-Making Unit (DMU) as a black box in that the internal processes are not examined in detail. In some situations, such as the measurement of performance of a set of supply chains, the DMU can be viewed as exhibiting a network structure. A significant body of recent literature has examined a particular form of network structure, namely, where the DMU is a two-stage serial process in which the outputs from the first stage are intermediate variables that serve as inputs to the second stage. The current article extends this idea to include those situations where outputs from the second stage can be fed back as inputs to the first stage. Such feedback variables thus serve a dual role. Models are developed for examining performance in this feedback setting and are illustrated using an application involving the measurement of performance of a set of Chinese universities.

In many applications of data envelopment analysis (DEA) approach, there is often a fixed cost or resource which is imposed on all decision making units (DMUs). One nature question then is: how can this cost or resource be assigned in a reasonable way to various DMUs? In this paper, we present a new DEA-based method for obtaining this cost or resource allocation. To illustrate our methods, numerical results for an example from the literature are presented.

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

This paper seeks to measure and evaluate the productive efficiency of health‐care organizations (hospitals) using a multicriteria Data Envelopment Analysis (DEA) methodology. DEA is a special linear programming model for deriving the comparative efficiency of multiple‐input multiple‐output Decision‐Making Units (DMUs). DEA provides management with information regarding the relatively best practice hospitals in the observation set and locates the relatively inefficient hospitals by comparison with the best practice ones. In addition, it indicates the magnitude of these inefficiencies. The paper selects a set of inputs and outputs for 15 hospitals for a three‐year period. The hospital input measures included are: number of bed days, number of physicians, and number of health personnel. The output measures included are: number of patient days, number of minor operations, and number of major operations. The DEA models are solved using an IBM PC optimization modeling system called LINDO (linear, interactive, discrete optimizer).

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.