Fuzzy data envelopment analysis approach based on sample decision making units

The conventional data envelopment analysis （DEA） measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit （DMU） with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit （FSDEA）. Five eval- uation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches.

Ranking approach of cross-efficiency based on improved TOPSIS technique

The cross-efficiency evaluation method is reviewed which is developed as a data envelopment analysis （DEA） extensive tool. The cross-efficiency evaluation method is utilized to identify the decision making unit （DMU） with the best practice and to rank the DMUs by their respective cross-efficiency scores. The main drawbacks of the cross-efficiency evaluation method when the ultimate average cross-efficiency scores are used to evalu- ate and rank the DMUs are also pointed out. With the research gap, an improved technique for order preference by similarity to ideal solution （TOPSIS） is introduced to rank the crossfficiency by eliminating the average assumption. Finally, an empirical example is illustrated to examine the validity of the proposed method.

Classifying and ranking DMUs in interval DEA

During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more discriminative, this paper builds an Interval Modified DEA (IMDEA) model based on MDEA. Furthermore, models of obtaining upper and lower bounds of the efficiency scores for each DMU are set up. Based on this, the DMUs are classified into three types. Next, a new order relation between intervals which can express the DM’s preference to the three types is proposed. As a result, a full and more convictive ranking is made on all the DMUs. Finally an example is given.

Application of a mixed DEA model to evaluate relative efficiency validity

Data envelopment analysis(DEA) model is widely used to evaluate the relative efficiency of producers. It is a kind of objective decision method with multiple indexes. However, the two basic models frequently used at present, the C2R model and the C2GS2 model have limitations when used alone,resulting in evaluations that are often unsatisfactory. In order to solve this problem, a mixed DEA model is built and is used to evaluate the validity of the business efficiency of listed companies. An explanation of how to use this mixed DEA model is offered and its feasibility is verified.

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.

Comparison of Efficiencies of Enterprises Engaged in Food Sector in Turkey by Data Envelopment Analysis Taking Account of Their Efficiencies and Rantability Rates

Enterprises are in competition with other enterprises operating in their fields. In today＇s world, enterprises need to increase their efficiency and productivity, the values of which, if below market conditions, they need to raise. Data Envelopment Analysis, widely utilized in efficiency and productivity measurements is a method that measures the efficiency of one unit relatively, taking more than one input determines the efficiency of units with similar inputs and outputs and output simultaneously. DEA interactively Thus, not only the efficient units are determined, but an opinion is offered as to how the inefficient units can be improved. In this study, 12 enterprises that were among the first 500 enterprises in Turkey in 2008, active in Istanbul Stock Exchange and operating in food sector through Data Envelopment Analysis. In the model, activity rates were used as input values, rantability rates were used as output values. In conclusion of simultaneous analysis of input and output values, efficient and inefficient enterprises were determined.

A COMPREHENSIVE DEA APPROACH FOR THE RESOURCE ALLOCATION PROBLEM BASED ON SCALE ECONOMIES CLASSIFICATION

This paper is concerned with the resource allocation problem based on data envelopment analysis （DEA） which is generally found in practice such as in public services and in production process. In management context, the resource allocation has to achieve the effective-efficient-equality aim and tries to balance the different desires of two management layers： central manager and each sector. In mathematical programming context, to solve the resource allocation asks for introducing many optimization techniques such as multiple-objective programming and goal programming. We construct an algorithm framework by using comprehensive DEA tools including CCR, BCC models, inverse DEA model, the most compromising common weights analysis model, and extra resource allocation algorithm. Returns to scale characteristic is put major place for analyzing DMUs＇ scale economies and used to select DMU candidates before resource allocation. By combining extra resource allocation algorithm with scale economies target, we propose a resource allocation solution, which can achieve the effective-efficient-equality target and also provide information for future resource allocation. Many numerical examples are discussed in this paper, which also verify our work.

Evaluation and Ranking DMUs in the Presence of Both Undesirable and Ordinal Factors in Data Envelopment Analysis

In the last decade,ranking units in data envelopment analysis(DEA) has become the interests of many DEA researchers and a variety of models were developed to rank units with multiple inputs and multiple outputs.These performance factors(inputs and outputs) are classified into two groups:desirable and undesirable.Obviously,undesirable factors in production process should be reduced to improve the performance.Also,some of these data may be known only in terms of ordinal relations.While the models developed in the past are interesting and meaningful,they didn t consider both undesirable and ordinal factors at the same time.In this research,we develop an evaluating model and a ranking model to overcome some deficiencies in the earlier models.This paper incorporates undesirable and ordinal data in DEA and discusses the efficiency evaluation and ranking of decision making units(DMUs) with undesirable and ordinal data.For this purpose,we transform the ordinal data into definite data,and then we consider each undesirable input and output as desirable output and input,respectively.Finally,an application that shows the capability of the proposed method is illustrated.

Improving the performance of a medical imaging center through simulation and fuzzy DEA

Given that various operational methods can be considered for a system,applying changes to study the results and determine the best operational technique is costly and disrupts the system.Thus,to analyze and evaluate the outcomes of employing different operational methods,simulation is utilized.In this study,to improve the performance of a medical imaging center(MIC),first,this center was simulated and then,different scenarios were defined and simulated.Subsequently,using the simulation results and for evaluating the scenarios through Data Envelopment Analysis(DEA),each scenario was considered as a decision making unit(DMU).Since simulation results of each scenario are uncertain and differ for different iterations,a method was utilized capable of using the results of all iterations.On the other hand,indexes such as satisfaction,patient’s appropriate waiting,helpful servicing with the least number nurses and the maximum number services that have fuzzy nature,are important factors for MIC.Therefore,using the simulation results and considering the system manager’s idea,a method was proposed for determining the fuzzy values of these indexes.Next,a ranking model and an algorithm were proposed for determining a unique ranking and introducing the best scenario to improve the performance of MIC.