Academic Journal
A DEA-ANN framework based in Improved Grey Wolf Algorithm to evaluate the performance of container terminal.
| العنوان: | A DEA-ANN framework based in Improved Grey Wolf Algorithm to evaluate the performance of container terminal. |
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| المؤلفون: | Fri, Mouhsene, Douaioui, Kaoutar, Tetouani, Samir, Mabrouki, Charif, Semma, El Alami |
| المصدر: | IOP Conference Series: Materials Science and Engineering ; volume 827, issue 1, page 012040 ; ISSN 1757-8981 1757-899X |
| بيانات النشر: | IOP Publishing |
| سنة النشر: | 2020 |
| الوصف: | Managing the performance of port container terminals is one of the major challenges in the supply chain management. In response to this challenge, we propose a new framework to assess managers in evaluating the global performance of operations in port container terminals. The new framework integrates the Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). The DEA is used to compute the efficient score of the system. In ANN, we use the improved grey wolf optimizer based on Levy’s flights to improve learning. In order to prove the efficiency of our model, we apply the framework in 2 ports container terminal: Tangier Med Port and Casablanca Port. The result is compared to standard algorithms and outperforms the cited algorithm in order to avoid local minima. The new trainer improved grey wolf optimizer is also evaluated using four known classification datasets and on three approximation functions datasets. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.1088/1757-899x/827/1/012040 |
| DOI: | 10.1088/1757-899X/827/1/012040/pdf |
| DOI: | 10.1088/1757-899X/827/1/012040 |
| الاتاحة: | http://dx.doi.org/10.1088/1757-899x/827/1/012040 https://iopscience.iop.org/article/10.1088/1757-899X/827/1/012040/pdf https://iopscience.iop.org/article/10.1088/1757-899X/827/1/012040 |
| Rights: | http://creativecommons.org/licenses/by/3.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining |
| رقم الانضمام: | edsbas.56B2981F |
| قاعدة البيانات: | BASE |
| DOI: | 10.1088/1757-899x/827/1/012040 |
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