Benchmarking in Tourism Destination, Keeping in Mind the Sustainable Paradigm

Tourism destination benchmarking and the assessment of tourism management performances are a crucial and challenging task in the direction of evaluating tourism sustainability and reshaping tourism activities. However, assessing tourism management efficiency per se may not provide enough information concerning long-term performances, which is what sustainability is about. Natural resources management should therefore be included in the analysis to provide a more exhaustive picture of long-run sustainable efficiency and tourism performances. Indeed, while the environmental endowment of a site is a key feature in tourism destination comparison, what really matters is its effective management. Therefore, in this paper we assess and compare tourism destinations, not only in terms of tourism services supply, but also in terms of the performance of environmental management. The proposed efficiency assessment procedure is based on Data Envelopment Analysis (DEA). DEA is a methodology for evaluating the relative efficiency when facing multiple input and output. Although the methodology is extremely versatile, for the sake of exemplification, in this paper it is applied to the valuation of sustainable tourism management of the twenty Italian regions.Data envelopment analysis, Sustainable tourism indicators

A Ranking Method Based on Common Weights and Benchmark Point

The highest efficiency score 1 (100% efficiency) is regarded as a common benchmark for Decision Making Units (DMUs). This brings about the existence of more than one DMU with the highest score. Such a case normally occurs in all Data Envelopment Analysis (DEA) models and also in all the Common Set of Weights (CSWs) methods and it may lead to the lack of thorough ranking of DMUs. And ideal DMU based on its specific structure is a unit that no unit would do better than. Therefore, it can be utilized as a benchmark for other units. We are going to take advantage of this feature to introduce a linear programming problem that will produce CSWs. The proposed method assures that the efficiency of all the units is less than that of the benchmark unit. As a result, it provides a comprehensive ranking of DMUs. Moreover, the proposed method is also noteworthy regarding computation. A numerical example is suggested to clarify and explain the proposed method and compare it to two other CSWs methods. Finally, 33 universities in Iran were ranked and compared using the proposed method

Nonparametric Efficiency Estimation in Stochastic Environments (II)

We consider the issues of noise-to-signal estimation, finite sample performance andhypothesis testing for the nonparametric efficiency estimation technique proposed inCherchye, L., T. Kuosmanen and G. T. Post (2001) 'Nonparametric efficiencyestimation in stochastic environments', forthcoming in Operations Research. Inaddition, we apply the technique for analyzing European banks.hypothesis testing;European banks;noise-to-signal estimation;nonparametric efficiency estimation;finite sample performance

Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster

Multi-Criteria versus Data Envelopment Analysis for Assessing the Performance of Biogas Plants

This paper compares multi-criteria decision aiding (MCDA) and data envelopment analysis (DEA) approaches for assessing renewable energy plants, in order to determine their performance in terms of economic, environmental, and social criteria and indicators. The case is for a dataset of 41 agricultural biogas plants in Austria using anaerobic digestion. The results indicate that MCDA constitutes an insightful approach, to be used alternatively or in a complementary way to DEA, namely in situations requiring a meaningful expression of managerial preferences regarding the relative importance of evaluation aspects to be considered in performance assessment.Multi-criteria decision analysis; DEA; Renewable energy; Biogas

Evaluation of performance of European cities with the aim of increasing quality of life

Tese de Doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Robust optimization in data envelopment analysis: extended theory and applications.

Performance evaluation of decision-making units (DMUs) via the data envelopment analysis (DEA) is confronted with multi-conflicting objectives, complex alternatives and significant uncertainties. Visualizing the risk of uncertainties in the data used in the evaluation process is crucial to understanding the need for cutting edge solution techniques to organizational decisions. A greater management concern is to have techniques and practical models that can evaluate their operations and make decisions that are not only optimal but also consistent with the changing environment. Motivated by the myriad need to mitigate the risk of uncertainties in performance evaluations, this thesis focuses on finding robust and flexible evaluation strategies to the ranking and classification of DMUs. It studies performance measurement with the DEA tool and addresses the uncertainties in data via the robust optimization technique. The thesis develops new models in robust data envelopment analysis with applications to management science, which are pursued in four research thrust. In the first thrust, a robust counterpart optimization with nonnegative decision variables is proposed which is then used to formulate new budget of uncertainty-based robust DEA models. The proposed model is shown to save the computational cost for robust optimization solutions to operations research problems involving only positive decision variables. The second research thrust studies the duality relations of models within the worst-case and best-case approach in the input \u2013 output orientation framework. A key contribution is the design of a classification scheme that utilizes the conservativeness and the risk preference of the decision maker. In the third thrust, a new robust DEA model based on ellipsoidal uncertainty sets is proposed which is further extended to the additive model and compared with imprecise additive models. The final thrust study the modelling techniques including goal programming, robust optimization and data envelopment to a transportation problem where the concern is on the efficiency of the transport network, uncertainties in the demand and supply of goods and a compromising solution to multiple conflicting objectives of the decision maker. Several numerical examples and real-world applications are made to explore and demonstrate the applicability of the developed models and their essence to management decisions. Applications such as the robust evaluation of banking efficiency in Europe and in particular Germany and Italy are made. Considering the proposed models and their applications, efficiency analysis explored in this research will correspond to the practical framework of industrial and organizational decision making and will further advance the course of robust management decisions

Using DEA and VEA to Evaluate Quality of Life in the Mid-Atlantic States

In this study we use data envelopment analysis (DEA) and an extension of DEA called value efficiency analysis (VEA) to explore the “"production”" of quality of life within counties in the mid-Atlantic region and the extent to which production frontiers and efficiency differ between rural and urban counties. These methods allow us to identify counties that are inefficient in their quality of life production, and to rank (using DEA) those counties according to their distance from a performance standard established by other observed counties(using VEA), or by a single unit designated as "“most preferred"(using VEA).”data envelopment analysis, value efficiency analysis, quality of life, Community/Rural/Urban Development,