Efficiency analysis in bi-level on fuzzy input and output

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.

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.

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

Data Envelopment Analysis (DEA) technique is considered one of the most appropriate tool for assessing performance through calculating the technical efficiencies of a collection of related comparable organizations in transforming inputs into outputs. The conventional DEA methods require accurate measurement of both the inputs and outputs. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague, i.e. fuzzy. Imprecise evaluations may be the result of unquantifiable, incomplete and non-obtainable information. From the literature of fuzzy DEA applications, most if not all developed models considered all input and output variables as fuzzy, through adopting either the traditional DEA model or the output version of the model, and solved using the α-level approach. Accordingly, the main aim of this paper is to develop a Fuzzy Input Oriented DEA Model that considers a mix of both fuzzy and deterministic output and/or input variables to be solved using the α-cut approach. The developed model algorithm is divided into three stages; it starts by defining the membership function for the fuzzy variables (assumed triangular), then finding the α-cuts for the fuzzy variables, and finally calculating the relative efficiency for each decision making unit (DMU). The model is demonstrated through an illustrative example.

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.

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

Data envelopment analysis (DEA) has been widely applied in the performance evaluation of environment or economy recently. If economic and environmental systems exist simultaneously in a decision making unit (DMU), conventional DEA models generally treat the DMU as a black box, and the internal relationship of DMU is often ignored. Thus, the performance of DMUs may be overestimated. In order to solve this problem, a new two-stage multiplier DEA approach is proposed in this paper. The new model can not only obtain the efficiency score of each system, but also consider the intermediate outputs when assessing the overall efficiency of the two systems. In order to test the effectiveness of this approach, a numerical example of 30 provinces in China is illustrated.

Unoriented two-stage DEA: The case of the oscillating intermediate products

In some real-world applications, performance assessment often has to be considered under uncertainty with fuzzy input and output. Data envelopment analysis (DEA) is a new and effective approach to measuring the relative efficiency of decision-making units concerned with performance evaluation. Recently, DEA has studied with fuzzy input and output. In this paper, we present a computational procedure using a robust ranking method to formulate and solve fuzzy DEA models. Based on this approach, we convert the fuzzy DEA input and output from fuzzy form to crisp. Also, a case study has been presented as in [6] to assess the performance of eight manufacturing enterprises with fuzzy data.

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.

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.

Free disposal hull efficiency scores of units with network structures

Environmental product declarations (EPDs) are standardized tools based on life cycle assessment (LCA) to communicate and compare environmental performance of products according to well-defined product category rules (PCRs). However, despite the comparability enabled by the PCRs, the use of this information for benchmarking is still challenging, since there is still no consensus or standardization regarding techniques and procedures to be adopted. Therefore, here, we suggest and apply a framework to benchmark and develop a ranking system for the products based on data from EPDs. The proposed framework is based on efficiency assessment using data envelopment analysis (DEA). The main advantages of DEA are that it does not require potentially non-scientific factors, e.g., for normalization and that it provides an efficiency score based on all the used indicators, rather than just based on a subset of them. A five-step benchmarking framework is presented which includes data collection from EPDs, statistical analyses, selection of the variables, application of DEA, and cluster analysis to establish the environmental performance ranking on a scale from A (best) to E (worst). In order to illustrate the applicability of the proposal, two case studies of different product categories are presented: bakery products and insulation materials. In the first case, 72 bakery products are evaluated of which 9 were ranked in the category A, while 9 other products are considered the most inefficient and were ranked in category E. For insulation materials, 89 products are evaluated and 9 are categorized as A of which 5 are considered efficient, while the category E comprises 20 products. The results obtained through DEA are compared with those from other approaches (being internal normalization and external normalization, respectively, each with subsequent aggregation) to obtain single scores. There are significant differences in the results obtained from the three approaches, especially when dealing with trade-offs, because DEA does not relate the scores to specific values, but to a so-called “efficiency frontier”. The use of DEA allows an analysis based on the efficiency of the products in terms of environmental performance and can be applied using data from EPDs. The framework proposed allows benchmarking of the results and opens a new perspective to contribute to the issue of limitations in the communication function of EPDs. However, DEA is limited to analyzing the efficiency of a given product in relation to a pre-determined group.

Closest target setting for two-stage network system: An application to the commercial banks in China