Chance Constrained Efficiency Evaluation

Olesen, Ole Bent; Petersen, Niels Christian

doi:10.1287/mnsc.41.3.442

Cited by 280 publications

(116 citation statements)

References 19 publications

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“…Among them, Sengupta (1990) can be considered as one of the pioneering ones. Later, Olesen and Petersen (1995) and Kneip and Simar (1996) also proposed alternative versions of a stochastic DEA model. Finally, Sengupta (2000a) and Huang and Li (2001) have developed more refined stochastic DEA models.…”

Section: New Developments: Statistical Inference Bootstrapping and Smentioning

confidence: 99%

Economic Efficiency and Frontier Techniques

Zamorano

2004

Journal of Economic Surveys

320

179

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Abstract. Most of the literature related to the measurement of economic efficiency has based its analysis either on parametric or on non-parametric frontier methods. The choice of estimation method has been an issue of debate, with some researchers preferring the parametric and others the non-parametric approach. The aim of this paper is to provide a critical and detailed review of both core frontier methods. In our opinion, no approach is strictly preferable to any other. Moreover, a careful consideration of their main advantages and disadvantages, of the data set utilized, and of the intrinsic characteristics of the framework under analysis will help us in the correct implementation of these techniques. Recent developments in frontier techniques and economic efficiency measurement such as Bayesian techniques, bootstrapping, duality theory and the analysis of sampling asymptotic properties are also considered in this paper.

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Section: New Developments: Statistical Inference Bootstrapping and Smentioning

confidence: 99%

Economic Efficiency and Frontier Techniques

Zamorano

2004

Journal of Economic Surveys

320

179

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“…To tackle incomplete precise and imprecise data in DEA, three major approaches including "fuzzy DEA" (see e.g., Sengupta 1992;Hatami-Marbini et al 2017a, 2017b, 2017c, "interval DEA" (see e.g., Cooper et al, 1999;Toloo et al, 2008, Toloo, 2014Hatami-Marbini et al 2014) and "stochastic DEA" (see e.g., Land et al 1993, Olesen and Petersen 1995 have dominated the literature. This paper places emphasis on fuzzy DEA approach to conquer the uncertainty in the performance evaluation process.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking with network dea in a fuzzy environment

Hatami-Marbini

2019

RAIRO-Oper. Res.

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Abstract:Benchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has practically and theoretically developed in distinct fields such as banking, education, health and so on, supply chain benchmarking across multiple echelons that includes certain characteristics such as intermediate measure differs from other fields. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying the organizations. We finally provide a case study of the airport and travel sector to elucidate the details of the proposed method in this study. All additions and changes to the original manuscript are highlighted in this revision. AbstractBenchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has practically and theoretically developed in distinct fields such as banking, education, health and so on, supply chain benchmarking across multiple echelons that includes certain characteristics such as intermediate measure differs from other fields. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying ...

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“…This approach is intractable because it can only incorporate the uncertainty in outputs rather both inputs and outputs (Cooper et al, 1996;Olesen, 2006;Olesen and Petersen, 1995).…”

Section: Introductionmentioning

confidence: 99%

DCBA-DEA: A Monte Carlo Simulation Optimization Approach for Predicting an Accurate Technical Efficiency in Stochastic Environment

Deng

Wong²

2014

Industrial Engineering and Management Systems

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This article describes a 2-in-1 methodology utilizing simulation optimization technique and Data Envelopment Analysis in measuring an accurate efficiency score. Given the high level of stochastic data in real environment, a novel methodology known as Data Collection Budget Allocation-Data Envelopment Analysis (DCBA-DEA) is developed. An example of the method application is shown in banking institutions. In addition to the novel approach presented, this article provides a new insight to the application domain of efficiency measurement as well as the way one conducts efficiency study.

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Chance Constrained Efficiency Evaluation

Cited by 280 publications

References 19 publications

Economic Efficiency and Frontier Techniques

Economic Efficiency and Frontier Techniques

Benchmarking with network dea in a fuzzy environment

DCBA-DEA: A Monte Carlo Simulation Optimization Approach for Predicting an Accurate Technical Efficiency in Stochastic Environment

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