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.

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.