نتایج جستجو برای: dea target setting common benchmarking reference hyperplane sequential targets

تعداد نتایج: 1698458  

Journal: :International Journal of Forest Engineering 2021

The forestry sector is constantly looking for ways making data-driven decisions and improving efficiency. application of Data Envelopment Analysis (DEA) Stochastic Frontier (SFA) allow the users to go beyond at simple key performance indicators. Benchmarking one most common tools in business efficiency competitiveness. This study searched benchmarking studies Web Science until December 2020. It...

Journal: :Knowl.-Based Syst. 2011
Mei-Chi Lai Hao-Chen Huang Wei-Kang Wang

First developed by Xerox in 1979, benchmarking provides measurement and comparison to improve processes and achieve higher performance. Benchmarking has proven a powerful tool for total quality management and process improvement. Successful benchmarking implementation is based on an effective benchmarking tool. To effectively implement benchmarking processes, this work proposes an integrated fr...

2007
F. Hosseinzadeh

Data envelopment analysis technique which is developed based on the mathematical programming, evaluates the relative efficiency of a set of homogeneous decision making units. This paper shows the method of Discriminant Analysis (DA), on Imprecise Data by Data Envelopment 724 F. Hosseinzadeh Lotfi et al Analysis (DEA). DEA-Discriminant Analysis (DEA-DA) is designed to identify the existence or n...

2013
Per AGRELL Zahra GHELEJ BEIGI Kobra GHOLAMI Adel HATAMI-MARBINI Farhad HOSSEINZADEH LOTFI Per J. AGRELL

Data envelopment analysis (DEA) is a powerful tool for measuring the relative efficiencies of a set of decision making units (DMUs) such as schools and bank branches that transform multiple inputs to multiple outputs. In centralized decision-making systems, management normally imposes common resource constraints such as fixed capital, budgets for operating capital and staff count. In consequenc...

2014
Hongmei Jiang Lingling An Veerabhadran Baladandayuthapani Paul Livermore Auer

of cancer data, including one or more of the following topics: Random Forest Algorithms § § Fuzzy-Set Analysis § § Non-Linear Signal Processing § § Bootstrapping Methods § § Imputation Algorithms § § Bayesian Classifiers § § Support Vector Machines § § Time-to-Event Models § § K-Means Cluster Analysis § § Discriminant Analysis Classifiers § § K-Nearest Neighbor Methods § § Multiple Comparison S...

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