Computational strategy for Russell measure in DEA: Second-order cone programming |
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Authors: | Toshiyuki Sueyoshi Kazuyuki Sekitani |
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Affiliation: | 1. New Mexico Institute of Mining and Technology, Department of Management, 801 Leroy Place, Socorro, NM 87801-4796, USA;2. National Cheng Kung University, Department of Industrial and Information Management, Tainan, Taiwan, ROC;3. Shizuoka University, Department of Systems Engineering, Johoku 3-5-1, Hamamatsu, Shizuoka, 432-8561, Japan |
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Abstract: | In aggregation for data envelopment analysis (DEA), a jointly measured efficiency score among inputs and outputs is desirable in performance analysis. A separate treatment between output-oriented efficiency and input-oriented efficiency is often needed in the conventional radial DEA models. Such radial measures usually need to measure both that a current performance attains an efficiency frontier and that all the slacks are zero on optimality. In the analytical framework of the radial measure, Russell measure is proposed to deal with such a difficulty. A major difficulty associated with the Russell measure is that it is modeled by a nonlinear programming formulation. Hence, a conventional linear programming algorithm, usually applied for DEA, cannot solve the Russell measure. This study newly proposes a reformulation of the Russell measure by a second-order cone programming (SOCP) model and applies the primal–dual interior point algorithm to solve the Russell measure. |
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Keywords: | DEA Second-order cone programming Interior point method |
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