Probabilistically constrained models for efficiency and dominance in DEA |
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| Authors: | ME Bruni D Conforti P Beraldi E Tundis |
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| Institution: | 1. Dipartimento di Elettronica, Informatica, Sistemistica, University of Calabria, Via P. Bucci 41C, 87030 Rende (Cosenza), Italy;2. Dipartimento di Informatica e Studi Aziendali, University of Trento, Via Inama 5, 38100 Trento (Italy);1. Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran;2. Department of Business and Management Science, NHH Norwegian School of Economics, 5045 Bergen, Norway;1. Department of Electrical and Computer Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8S 4K1, Canada;2. Department of Electrical and Computer Engineering, University of British Columbia, BC, Canada;1. MAP5, UMR 8145 CNRS, Sorbonne Paris Cité, Paris Descartes University, France;2. Sorbonne Universités, Université Pierre et Marie Curie, UMR 7599 CNRS, Laboratoire de Probabilités et Modèles aléatoires, France;1. Department of Business, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain;2. Faculty of Economic and Administrative Sciences, Universidad Católica de Colombia, Bogotá, Colombia;3. Department of Economics, Universitat Jaume I, Castelló, Spain |
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| Abstract: | This paper proposes a stochastic model for data envelopment analysis (DEA), based on the theory of joint probabilistic constraints, which can be used with general multivariate distribution functions. The key assumption is that the random variables representative of the uncertain data follow a discrete distribution or that a discrete approximation of continuous distribution is available. Under this assumption, mixed integer linear models are formulated to tackle, rather originally, dependencies among DMUs inputs, outputs and inputs–outputs through the theory of joint probabilistic constraints. The features of the model are illustrated through an application for the performance evaluation of screening units. |
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