Adel Hatami-Marbini

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Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes(More)
Many real-world decision problems involve conflicting systems of criteria, uncertainty and imprecise information. Some also involve a group of decision makers (DMs) where a reduction of different criteria decisions by making the process more explicit, rational and efficient. One family of MCDA models uses what is known as ‘‘outranking relations’’ to rank a(More)
0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.04.108 ⇑ Corresponding author. Tel.: +1 215 951 1129; fax E-mail addresses: tavana@lasalle.edu (M. Tavana) vain.be, adel_hatami@yahoo.com (A. Hatami-Marbini URL: http://lasalle.edu/~tavana (M. Tavana). 1 Tel.: +32 486 707387; fax: +32 10 47 4301. Human spaceflight mission planning is(More)
Data envelopment analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Crisp input and output data are fundamentally indispensable in traditional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or(More)
Department of Industrial Engineering, Khajeh Nasir Toosi University, Tehran, Iran b Louvain School of Management, Center of Operations Research and Econometrics (CORE), Universite Catholique de Louvain, 34 Voie du Roman Pays, B-1348 Louvain-le-Neuve, Belgium Management Department, Lindback Distinguished Chair of Information Systems, La Salle University,(More)
Data envelopment analysis (DEA) is a data-driven non-parametric approach for measuring the efficiency of a set of decision making units (DMUs) using multiple inputs to generate multiple outputs. Conventionally, DEA is used in ex post evaluation of actual performance, estimating an empirical best-practice frontier using minimal assumptions about the shape of(More)
Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and outputs. Conventional DEA models assume that inputs and outputs are measured by exact values on a ratio scale. However, the observed values of the input and output data in real-world problems(More)
Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. In the conventional DEA, all the data assume the form of specific numerical values. However, the observed values of the input and output data in real-life problems are(More)