International Journal of Industrial Engineering & Production Research

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Data envelopment analysis operates as a tool for appraising the relative efficiency of a set of homogenous decision making units. This methodology is applied widely in different contexts. Regarding to its logic, DEA allows each DMU to take its optimal weight in comparison with other DMUs while a similar condition is considered for other units. This feature is a bilabial characteristic which optimizes the performance of units in one hand. This flexibility on the other hand threats the comparability of different units because different weighting schemes are used for different DMUs. This paper proposes a unified model for determination of a common set of weights to calculate DMUs efficiency. This model is developed based on a multi objective fractional linear programming model that considers the original DEA's results as ideal solution and seeks a set of common weights that rank the DMUs and increase the model's discrimination power. Comparison of the proposed method with some of the previously presented models has shown its advantages as a DMUs ranking model.

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This research aims at presenting a consolidated model of data envelopment analysis (DEA) technique and value engineering to select the best manufacturing methods for gate valve covers and ranking the methods using TOPSIS.To do so, efficiency evaluation indices were selected based on the value engineering approach and different manufacturing methods were evaluated using DEA technique.Finally, effective methods were ranked based on TOPSIS. Accordingly, 48 different methods were identified for manufacturing the part. The DEA results showed that only 12 methods have complete efficiency. Meanwhile manufacturing method No. 32 (A216 WCB casting purchased from Chinese market as the raw material, machining by CNC+NC and drilling by radial drill) was ranked the first.Major limitations of the research include time limitations, place limitation, lack of access to the standards adaptability index in different machining and drilling methods, limitation on evaluating all parts of a product, limitation on a technique evaluating efficiency and ranking, and mere satisfying with superior indices in each factor of value engineering. Most previous studies only evaluated efficiency of manufacturing methods based on a single approach.By applying value engineering, which is in fact a combination of three approaches (including quality approach, functional, and cost approaches), the present research provided a far more comprehensive model to evaluate manufacturing methods in industrial.

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Abstract
Background: In this paper healthcare condition of 31 countries that are the members of Organization for Economic and Co-operative Development (OECD) is measured by considering 14 indicators that are relevant to three main pillars of sustainable development.
Method: To estimate the efficiency scores, Principle Component Analysis-Data Envelopment Analysis PCA-DEA additive model in both forms of envelopment and multiplier is used to determine efficiency scores and also to define benchmarks and improvement plan for the inefficient countries. Then Decision Tree Analysis is also used to realize that which factors were the most influential ones to make a county an efficient Decision Making Unit (DMU).
Results: According to the PCA-DEA additive model, among 31 OECD countries, 16 countries have become inefficient, that USA have taken the lowest efficiency score, and among efficient countries Iceland could be considered as a paragon which has the highest frequency between the countries that are defined as the benchmarks. Decision tree analysis also show that exposure to PM2.5 is an influential factor on the efficiency status of countries.
Conclusion: This research gives an insight about the sustainable development and healthcare system and show the impressive effect of environmental and social factors like: exposure to PM2.5 and water quality, population insurance coverage, and AIDS on the healthcare efficiency of OECD countries

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So far, numerous studies have been developed to evaluate the performance of “Decision-Making Units (DMUs)” through “Data Envelopment Analysis (DEA)” and “Network Data Envelopment Analysis (NDEA)” models in different places, but most of these studies have measured the performance of DMUs by efficiency criteria. The productivity is considered as a key factor in the success and development of DMUs and its evaluation is more comprehensive than efficiency evaluation. Recently, studies have been developed to evaluate the productivity of DMUs through the mentioned models but firstly, the number of these studies especially in NDEA models is scarce, and secondly, productivity in these studies is often evaluated through the “productivity indexes”. These indexes require at least two time periods and also the two important elements of efficiency and effectiveness in these studies are not significantly evident. So, the purpose of this study is to develop a new approach in the NDEA models using “Multi-Objective Programming (MOP)” method in order to measure productivity of DMUs through efficiency and effectiveness “simultaneously, in one stage, in a period, and interdependently”. “Simultaneous and single-stage” study provides the advantage of sensitivity analysis in the model. One case study demonstrates application of the proposed approach in the branches of a Bank. Using proposed approach revealed that it is possible for a branch to be efficient by considering its subdivisions separately but not be efficient by considering the conjunction between its subdivisions. In addition, a branch may be efficient by considering the conjunction between its subdivisions but not be productive. Efficient branches are not necessarily productive, but productive branches are also efficient.