Parallel and hierarchical decomposition approaches for solving large-scale data envelopment analysis models
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Publication:1374270
DOI10.1023/A:1018941531019zbMath0891.90002MaRDI QIDQ1374270
Richard S. Barr, Matthew L. Durchholz
Publication date: 2 December 1997
Published in: Annals of Operations Research (Search for Journal in Brave)
Decision theory (91B06) Large-scale problems in mathematical programming (90C06) Applications of mathematical programming (90C90) Parallel numerical computation (65Y05)
Related Items (22)
Parallel processing of the Build Hull algorithm to address the large-scale DEA problem ⋮ Efficiency evaluation based on data envelopment analysis in the big data context ⋮ Using lexicographic parametric programming for identifying efficient units in DEA ⋮ Multidimensional frontier visualization based on optimization methods using parallel computations ⋮ Big data efficiency analysis: improved algorithms for data envelopment analysis involving large datasets ⋮ A nonparametric framework to detect outliers in estimating production frontiers ⋮ DEARank: a data-envelopment-analysis-based ranking method ⋮ Efficiency evaluation of very large-scale samples: data envelopment analysis with angle-index synthesis ⋮ Competing output-sensitive frame algorithms ⋮ RED: a new method for performance ranking of large decision making units ⋮ Generalizing cross redundancy in data envelopment analysis ⋮ A computational study of DEA with massive data sets ⋮ A dimensional decomposition approach to identifying efficient units in large-scale DEA models ⋮ Improving discrimination in data envelopment analysis without losing information based on Renyi's entropy ⋮ PARALLEL NETWORK COMPUTING APPROACH FOR DEA-RAM MEASUREMENT ⋮ Preprocessing DEA ⋮ Data envelopment analysis and big data ⋮ Determining radial efficiency with a large data set by solving small-size linear programs ⋮ Interior point methods in DEA to determine non-zero multiplier weights ⋮ DEA network computing in multi-stage parallel processes ⋮ Aggregation of inputs and outputs prior to data envelopment analysis under big data ⋮ A procedure for large-scale DEA computations
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