An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach
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- @Article{journals/nca/FallahpourOMKW16,
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author = "Alireza Fallahpour and Ezutah Udoncy Olugu and
Siti Nurmaya Musa and Dariush Khezrimotlagh and
Kuan Yew Wong",
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title = "An integrated model for green supplier selection under
fuzzy environment: application of data envelopment
analysis and genetic programming approach",
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journal = "Neural Computing and Applications",
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year = "2016",
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volume = "27",
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number = "3",
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pages = "707--725",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Green
supplier selection, Data envelopment analysis, DEA,
Artificial intelligence, AI, GP, Parametric analysis",
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ISSN = "0941-0643",
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bibdate = "2016-03-24",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca27.html#FallahpourOMKW16",
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DOI = "doi:10.1007/s00521-015-1890-3",
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size = "19 pages",
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abstract = "Supplier evaluation plays a critical role in a
successful supply chain management. Hence, the
evaluation and selection of the right suppliers have
become a central decision of manufacturing business
activities around the world. Consequently, numerous
individual and integrated methods have been presented
to evaluate and select suppliers. The current
literature shows that hybrid artificial intelligence
(AI)-based models have received much attention for
supplier evaluation. Integrated data envelopment
analysis-artificial neural network (DEA-ANN) is one of
the combined methods that have recently garnered great
attention from academics and practitioners. However,
DEA-ANN model has some drawbacks, which make some
limitation in the evaluation process. In this study, we
aim at improving the previous DEA-AI models by
integrating the Kourosh and Arash method as a robust
model of DEA with a new AI approach namely genetic
programming (GP) to overcome the shortcomings of
previous DEA-AI models in supplier selection. Indeed,
in this paper, GP provides a robust nonlinear
mathematical equation for the suppliers efficiency
using the determined criteria. To validate the model,
adaptive neuro-fuzzy inference system as a powerful
tool was used to compare the result with GP-based
model. In addition, parametric analysis and unseen data
set were used to validate the precision of the model.",
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notes = "Department of Mechanical Engineering, Faculty of
Engineering, University of Malaya, Kuala Lumpur,
Malaysia",
- }
Genetic Programming entries for
Alireza Fallahpour
Ezutah Udoncy Olugu
Siti Nurmaya Musa
Dariush Khezrimotlagh
Kuan Yew Wong
Citations