Relative Efficiency Of Decision Making Units Research Articles

A Data Envelopment Analysis Approach to the Assessment of Natural Parks' Economic Efficiency and Sustainability. The Case of Italian National Parks

Wilderness protection is a growing necessity for modern societies, and this is particularly true for areas where population density is extremely high, as for example Europe. Conservation, however, implies very high opportunity costs. It is thus crucial to create incentives to efficient management practices, to promote benchmarking and to improve conservation management. In the present paper we propose a methodology based on Data Envelopment Analysis, DEA, a non parametric benchmarking technique specifically developed to assess the relative efficiency of decision-making units. In particular, the objective of the discussed methodology is to assess the relative efficiency of the management units of the protected area and to indicate how it could be improved, by providing a set of guidelines. The main advantage of this methodology is that it allows to assess the efficiency of natural parks' management not only internally (comparing the performance of the park to itself in time) but also by external benchmarking, thus providing new and different perspectives on potential improvements. Although the proposed methodology is fairly general, we have applied it to the context of Italian National Parks in order to produce a representative case study. Specifically, the choice of adequate cost and benefit indicators is a very important and delicate phase of any benchmark analysis. For this purpose, a questionnaire was used to investigate the opinions of Italian National Parks managers and stakeholders and to define the relevant indicators for the analysis. Finally, relevant policy implications for the case study are given.

Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection

A practical common weight multi-criteria decision-making (MCDM) methodology with an improved discriminating power for technology selection is introduced. The proposed MCDM methodology enables the evaluation of the relative efficiency of decision-making units (DMUs) with respect to multiple outputs and a single exact input. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed MCDM framework with that obtained by the cross-efficiency analysis, which is a well-known data envelopment analysis-based methodology. The results indicate that the proposed methodology enables further ranking of data envelopment analysis-efficient DMUs with a notable saving in computations compared with cross-efficiency analysis. Finally, the proposed MCDM framework is extended to incorporate ordinal as well as exact outputs, and an application is presented to illustrate the methodology.

MEASURING RAILWAY PERFORMANCE WITH ADJUSTMENT OF ENVIRONMENTAL EFFECTS, DATA NOISE AND SLACKS

Conventional data envelopment analysis (DEA) approaches (e.g., CCR model, 1978; BCC model, 1984) do not adjust the environmental effects, data noise and slacks while comparing the relative efficiency of decision-making units (DMUs). Consequently, the comparison can be seriously biased because the heterogeneous DMUs are not adjusted to a common platform of operating environment and a common state of nature. Although Fried et al. (2002, Journal of Productivity Analysis, 17, 157–174) attempted to overcome this problem by proposing a three-stage DEA approach, they did not account for the slack effects and thus also led to biased comparison. In measuring the productivity growth, Färe et al. (1994, American Economic Review, 84, 66–83) proposed a method to calculate the input or output distance functions. Similarly, they did not take environmental effects, statistical noise and slacks into account and thus also resulted in biased results. To correct these shortcomings, this paper proposes a four-stage DEA approach to measure the railway transport technical efficiency and service effectiveness, and a four-stage method to measure the productivity and sales capability growths, both incorporated with environmental effects, data noise and slacks adjustment. In the empirical study, a total of 308 data points, composed of 44 worldwide railways over seven years (1995-2001), are used as the tested DMUs. The empirical results have shown strong evidence that efficiency and effectiveness scores are overestimated, and productivity and sales capability growths are also overstated, provided that the environmental effects, data noise and slacks are not adjusted. Based on our empirical findings, important policy implications are addressed and amelioration strategies for operating railways are proposed.

Measuring the performance of ethical mutual funds: a DEA approach

The solidarity and social responsibility features that characterize the ethical mutual funds satisfy the fulfillment of humanitarian aims, but may lower the investment profitability. Hence, when we measure the performance of ethical mutual funds, we cannot disregard the ethical component. In this contribution, we propose a performance indicator that considers the expected return, the investment risk, the ethical component and the subscription and redemption costs together. The performance measure proposed is obtained using a data envelopment analysis (DEA) approach, which allows one to measure the relative efficiency of decision-making units in the presence of a multiple input–multiple output structure. The DEA performance indicator for ethical funds can be computed with different models, according to the nature of the ethical indicator that characterizes the socially responsible funds. In particular, a DEA categorical variable model seems appropriate.

Using dea to measure the relative efficiency of the service center and improve operation efficiency through reorganization

Data envelopment analysis (DEA) has become a practicable approach to evaluate the relative efficiencies of decision-making units in various contexts. This paper conducted a DEA study to measure the relative efficiencies of 17 service centers of the NAN-TOU electricity distribution district of Taiwan Power Company (TPC). In addition, this paper also investigated the alternatives for reorganizing the service centers via efficiency measurement. The results showed that the proposed reorganization alternatives have better efficiency scores. Based on DEA evaluations, the authors provided specific directions for the inefficient service centers to improve their operation efficiencies, and thus, maintain the competitive advantage of TPC in facing power market liberalization.

Assessing the relative efficiency of decision making units using DEA models with weight restrictions

In models of data envelopment analysis (DEA), an optimal set of input and output weights is generally assumed to represent the assessed decision making unit (DMU) in the best light in comparison to all the other DMUs. The paper shows that this may not be correct if absolute weight bounds or some other weight restrictions are added to the model. A consequence may be that the model will underestimate the relative efficiency of DMUs. The incorporation of weight restrictions in a maximin DEA model is suggested. This model can be further converted to more operational forms, which are similar to the classical DEA models.

Has livability of Japan gotten better for 1956-1990? : a DEA approach

This paper presents an approach for time-series livability assessment using DEA (Data Envelopment Analysis), a mathematical programming technique for measuring the relative efficiency of DMUs (Decision Making Units) with multiple inputs and multiple outputs. Regarding each year as a separate DMU in DEA, and replacing the inputs and the outputs with negative and positive social indicators respectively, we evaluate Japan's livability for the period 1956–1990. Results of the analysis using eight social indicators identify 20 DEA livable years out of the 35 and find eight best-balanced years. It is concluded that DEA, which enables non-uniform, multi-dimensional and relative evaluation, can be a valuable analytic tool in quality-of-life research as well.

A Procedure for Ranking Efficient Units in Data Envelopment Analysis

Data Envelopment Analysis (DEA) evaluates the relative efficiency of decision-making units (DMUs) but does not allow for a ranking of the efficient units themselves. A modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed. The procedure provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.