One of the primary concerns in banking industry is to measure the relative importance of banking industry using popular multi criteria decision making (MCDM) techniques such as data envelopment analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In this paper, we use these two MCDM techniques to measure the relative efficiencies of 16 private and governmental banks in terms of electronic payment. There are three inputs with DEA methods including the number of issued cards, the number of ATM machines and the number of POSs and there are two outputs including the number of successful ATMs and the number of successful POSs transactions. The proposed study of this paper uses the necessary data of one of Iranian provinces and the results of the implementation of DEA and TOPSIS have indicated that 9 out of 16 banks were efficient. Our study also indicates that mean of relative efficiency for private banks was 82% while this number was 75% for governmental banks.

Measuring the relative performance of insurance firms plays an important role in this industry. In this paper, we present a two-stage data envelopment analysis to measure the performance of insurance firms, which were active over the period of 2006-2010. The proposed study of this paper performs DEA method in two stages where the first stage considers five inputs and three outputs while the second stage considers the outputs of the first stage as the inputs of the second stage and uses three different outputs for this stage. The results of our survey have indicated that while there were 4 efficient insurance firms most other insurances were noticeably inefficient. This means market was monopolized mostly by a limited number of insurance firms and competition was not fare enough to let other firms participate in economy, more efficiently.

Ranking industry normally helps find hot sectors and attract potential investors to invest in appropriate plans. Ranking various industries is also a multiple criteria decision making problem. In this paper, we present an empirical investigation to rank different industries using the art of data envelopment analysis (DEA). The inputs of our proposed DEA model include capital, employment and importance coefficient and outputs are exports, ecological effects and added value. In addition, exports, value added and environmental investment are used as outputs of DEA method. Since the results of DEA may consider more than one efficient unit, so we implement Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique to rank efficient units. In our case study, there were 15 different sectors from various industries and the implementation of DEA technique recommends 8 efficient units. The implementation of TOPSIS among these efficient units has suggested that Chemical industry could be considered as the most attracting industry for investment.

There are many opportunities and challenges in the area of Indian technical education due to liberalization and globalization of economy. One of these challenges is how to assess the performance of technical institutions based on multiple criteria. The purpose of this paper is to describe and illustrate an application of a structured approach to determine relative performance and ranking of seven Indian Institutes of Technology (IITs) under multi-criteria environment. To evaluate the alternatives in respect to stakeholders’ preference we suggest a new methodology consisting of fuzzy AHP, DEA and TOPSIS. Fuzzy AHP technique is used to determine the weights of criteria and some linguistic terms are applied to assess performance under each criterion, then in order to determine the value of linguistic terms we use the data envelopment analysis (DEA) method. Finally TOPSIS method is used to aggregate performance scores under different criteria into an overall performance score for each institution and ranking the institution according to their overall performance score. The proposed fuzzy AHP–DEA–TOPSIS methodology is applicable to any multiple criteria decision making (MCDM) problem due to its generic nature.

This paper focuses on routing flexibility, which is the ability to manufacture a part type via several routes and/or to perform different operations on more than one machine. Specifically, the paper presents a comprehensive method for the measurement of routing flexibility, in a generic manufacturing system. The problem is approached in a modular way, starting from a basic set of flexibility indexes. These are progressively extended to include more comprehensive and complex routing attributes, such as: the average efficiency, the range and the homogeneous distribution of the alternative routes. Two procedures are finally proposed to compare manufacturing systems in terms of routing flexibility. The first one uses a vectorial representation of the previously defined indexes and the second one is based on data envelopment analysis, a multi-criteria decision making approach. The paper concludes with a numerical example, supported by discrete event simulation, which validates the proposed approach.

This study evaluates the relative efficiency of digital health adoption across ten major German cities and regions. In a landscape where digital transformation is critical, this research moves beyond mere technological assessment to determine which locales most effectively convert inputs into outputs. A multi-criteria decision-making framework is employed, integrating Data Envelopment Analysis (DEA) under both constant and variable returns-to-scale assumptions and the TOPSIS method. Key metrics include Infrastructure, Healthcare System Integration, Service Breadth, Regulatory Environment, and a critical cost factor, Accessibility & Equity. Results from DEA models highlight a system-wide scale inefficiency, yet identify specific efficient units under variable returns, including Berlin, Rhineland-Pfalz, Leipzig, and Aachen. The TOPSIS analysis, particularly when prioritizing cost-equity, reveals a distinct ranking: Rhineland-Pfalz, Leipzig, and Aachen emerge as top performers, while major economic hubs like Munich, Frankfurt, and Stuttgart demonstrate lower efficiency due to high costs and systemic fragmentation. The findings challenge the presumption that economic scale guarantees digital health efficiency, instead underscoring the superior performance of strategically coordinated and equity-focused models. This study provides policymakers with a robust framework for benchmarking and highlights governance and integration, not just investment, as the key levers for enhancing digital health system performance.

In this paper, we propose a method to measure the relative efficiency of different units of a publicly traded bank using data envelopment analysis. The study also measures the productivity of different banks using Malmquist index and the results are compared with relative efficiency of banks. The proposed model uses nine financial factors reported by the bank officials for both studies where five items are used for efficiency measurement and the other four items are used for productivity measurement. The results indicate that different banks perform inefficient in terms of efficiency but they have attempted to increase their productivity. We also analyze the results and give guidelines on how inefficient units could increase their efficiency by reducing their inputs or increasing their outputs

In this paper, we propose a multi criteria decision making technique to determine an efficient solution for quadratic assignment problem. The proposed method of this paper considers transportation cost, adjacency and separation as the most important criteria to find the efficient layout. A tabu search is used to generate a set of feasible solutions and for each solution, we evaluate various criteria. The proposed model of the paper uses DEA technique to choose the most efficient units among the feasible solutions. The implementation of the proposed method is demonstrated using some benchmark problems and the results are discussed in details.

The analysis of this research is based on the comparative technical efficiency processes of computed tomography (CT) and magnetic resonance imaging (MRI) service delivery in the seven corresponding regions of the National Health Service (NHS) in England. Two inputs—estimated numbers of radiographers and scanner systems—and one output—the volume of exams performed (2022/23)—were used in a Data Envelopment Analysis (DEA). Four distinct DEA models: CCR, BCC Input-Oriented, BCC Output-Oriented, and the Additive model were used to compute the efficiency. The outcome of the analysis was that the majority of the NHS regions witnessed the relative technical efficiency of a high level. For the MRI services, London and the East of England turned out to be the most efficient decision-making units (DMUs). The East of England and the Midlands scored the highest in efficiency for CT services. This implies that there might be certain factors like economies of scale, specialized high-throughput centers, and optimal resource allocation strategies that substantially contribute to such performance. The identified benchmarks, although performance is overall strong, provide health policy with insights. The results point out specific regions whose operational practices can act as a model for others, implying that targeted efficiency improvement is possible without the need for increasing the resource levels but rather through better use of existing assets.

This paper assesses the comparative technical efficiency of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) service delivery in all U.S. states and the District of Columbia, totaling 50. Data Envelopment Analysis (DEA) was applied and two inputs were used—the estimated number of scanners and radiographers—and one output—the estimated annual volume of exams. Efficiency was calculated according to the CCR, BCC Input-Oriented, BCC Output-Oriented, and Additive models. The investigation disclosed that almost every U.S. region performs relatively efficiently in a technical way to a great extent for MRI and CT services. Wyoming and Montana stood out as the best-evaluated decision-making units for MRI. In the case of CT, Montana once more was the winner. Consequently, these states with a small population manage to get the best results from their resources. The high scores all over the country signal a healthcare facility that is very good at turning imaging resources into the volume of service. The U.S. is rated highly in technical efficiency with respect to diagnostic imaging, but this is probably the result of a system that rewards high-volume service production. The study reveals an important difference between technical efficiency and wider value, implying that high performance in volume output is not automatically equal to cost-effectiveness or best patient outcomes. Quality and cost metrics should be incorporated in the future research.