Stochastic models and variable returns to scales in data envelopment analysis |
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| Affiliation: | 1. Department of Economics, University of Connecticut, 365 Fairfield Way, U-1063, Storrs, CT 06269-1063, United States;2. National Marine Fisheries Service, Northeast Fisheries Science Center, 166 Water St., Woods Hole, MA 02543, United States;3. Jianghan University, Wuhan, China;1. Dongguk Business School, Dongguk University—Seoul, 30, Pildong-ro 1-gil, Jung-gu, Seoul 100-715, South Korea;2. International Center for Auditing and Evaluation, Nanjing Audit University, Nanjing 211815, PR China;3. Robert A. Foisie School of Business, Worcester Polytechnic Institute, Worcester, MA 01609, USA;1. Faculty of Economics and Business Administration, Ghent University, Sint-Pietersplein 7, Gent 9000, Belgium;2. Traffic Management & Services Department, Infrabel, Frankrijkstraat 85, Brussels 1060, Belgium;3. IÉSEG School of Management, LEM (UMR-CNRS 9221), 3 Rue de la Digue, Lille 59000, France;4. Department of Economics, Katholieke Universiteit Leuven, Etienne Sabbelaan 51, Kortrijk 8500, Belgium;1. Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland, New Zealand;2. Department of Management Science, Lancaster University Management School, Lancaster, UK;3. Faculty of Information Science and Technology, The National University of Malaysia, Bangi Selangor, Malaysia;4. Department of Radiation Oncology, Auckland City Hospital, Auckland, New Zealand;5. Department of Accounting and Finance, Faculty of Business and Economics, University of Auckland, Auckland, New Zealand;6. Department of Radiation Oncology, Calvary Mater Newcastle and School of Physics and Mathematics, University of Newcastle, Newcastle, NSW, Australia;1. Department of Mathematics, College of Sciences, Shiraz University, Shiraz 71454, Iran;2. School of Business and Economics, Loughborough University, Loughborough LE11 3TU, UK |
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| Abstract: | Stochastic Data Envelopment Analysis (DEA) models were developed by taking random disturbances into account for the possibility of variations in input-output data structure. The stochastic efficiency measure of a Decision Making Unit (DMU) is defined via joint probabilistic comparisons of inputs and outputs with other DMUs, and can be characterized by solving a chance constrained programming problem. Deterministic equivalents are derived for both situations of multivariate symmetric random disturbances and a single random factor in the production relationships. An analysis of stochastic variable returns to scale is developed. |
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