Narrow Results

In real-life applications, there generally exist Decision Makers (DMs) who have preferences over outputs and inputs. Choosing appropriate weights for different criteria by DMs often arises as a problem. The Best-Worst Method (BWM) in Multiple Criteria Decision-Making (MCDM) depends on very few pairwise comparisons and just needs DMs to identify the most desirable and the least desirable criteria. Unlike MCDM, Data Envelopment Analysis (DEA) does not generally assume a priority for an output (an input) over any other outputs (inputs). The link between DEA and MCDM can be introduced by considering Decision-Making Units (DMUs) as alternatives, outputs as criteria to be maximized, and inputs as criteria to be minimized. In this study, we propose a linear programming model to embed DEA and BWM appropriately. We first propose a modified BWM linear programming model to satisfy all conditions that DMs can assume. We then illustrate how a conventional DEA model can be developed to include the BWM conditions. From our approach, the MCDM problem to obtain the optimal weights of different criteria are measured. At the same time, the relative efficiency scores of DMUs corresponding to the MCDM criteria are also calculated. We provide the foundation of measuring the efficiency scores when most desirable and the least desirable inputs and outputs are known. To show the process of the proposed approach, a numerical example (including 17 DMUs with seven inputs and outputs) is also discussed.

14 ranking methods based on multiple criteria are suggested for evaluating the performance of the bank branches. The methods are explained via an illustrative example, and some of them are applied to a real-life data for 23 retail bank branches in a large-scale private Turkish commercial bank.

Statistical properties of DEA methods for efficiency estimation are poorly understood and currently the best way forward must be to use bootstrap techniques. The article seeks to extend bootstrap methods to allow investigation of the properties of estimates of inefficiencies due to the slack in the use of resources as well as technical efficiency. In an empirical application, it is found that inefficiency due to slack is a small component of the overall inefficiency and that the DEA technical efficiency estimates have a small downward bias, with confidence intervals that are wide enough to suggest cautious interpretation.

Data envelopment analysis (DEA) is the most widely used non-parametric method in healthcare operation management to measure technical, productive, and allocative efficiency. As healthcare is characterized by complex production processes, we need some other subsequent techniques. Thus, integrated with DEA models, additional estimation procedures have been applied to evaluate efficiency. Although the literature on DEA is prevalent, there exists a lack of evidence in the studies using two-stage DEA in healthcare efficiency analysis. This chapter aims to review publications about two-stage DEA, which is a specific variation of conventional DEA, and to explore how two-stage DEA procedures are prevalent in healthcare. Investigating the state of the art of two-stage DEA models can add value for researchers who plan to conduct research using DEA. This chapter offers a rapid review and bibliometric analysis to explore publications regarding the relevant topic. The number of publications reached a peak in 2021. Review articles focused on various healthcare specialties. Seventeen articles were related to hospitals and healthcare centers, and tobit regression remained the primary choice of analysis for the dependent variable in 11 articles. It was widely used across various units, such as health regions, health systems, and patient-level treatment. Some concerns and controversies were addressed to improve validation and prove practical usefulness.

In the recent years, as a result of the economic crisis, there is a pressing need for new management tools and statistical methods to compare firms seeking better results. Comparison of firms with best observed performance is getting an increasing in importance due to the large amount of data which can be extracted from the Web. In this work a review of quantitative benchmarking techniques based on Data Envelopment Analysis is presented with examples derived from the widely available datasets.