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Scientific Journal of Maritime Research, Vol. 34 No. 2, 2020.

Original scientific paper

https://doi.org/10.31217/p.34.2.7

A hybrid framework for evaluating the performance of port container terminal operations: Moroccan case study

Mouhsene Fri orcid id orcid.org/0000-0002-2589-2303 ; Hassan First University of Settat, Faculté Sciences et Technique, Laboratoire Ingénierie, Management Industriel et Innovation, Settat, Morocco
Kaoutar Douaioui ; Hassan First University of Settat, Faculté Sciences et Technique, Laboratoire Ingénierie, Management Industriel et Innovation, Settat, Morocco
Nabil Lamii ; Hassan First University of Settat, Faculté Sciences et Technique, Laboratoire Ingénierie, Management Industriel et Innovation, Settat, Morocco
Charif Mabrouki orcid id orcid.org/0000-0002-3700-764X ; Hassan First University of Settat, Faculté Sciences et Technique, Laboratoire Ingénierie, Management Industriel et Innovation, Settat, Morocco
El Alami Semma ; Hassan First University of Settat, Faculté Sciences et Technique, Laboratoire Ingénierie, Management Industriel et Innovation, Settat, Morocco

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Abstract

This work intends to integrate artificial neural network (ANN) and data envelopment analysis (DEA) in a single framework to evaluate the performance of operations in the container terminal. The proposed framework is based on three steps. In the first step, a proposed identify the performance measures objectives and the indicators affecting the system. In the second step, the efficiency scores of the system are computed by using the Charnes Cooper and Rhodes (CCR) model (oriented inputs). In the last step, the Moth Search Algorithm (MSA) is employed as a new method for training the Feedforward Neural Network (FNN) to determine the efficiency scores. To demonstrate the efficacy of the proposed framework, two container terminals of Tangier and Casablanca are adopted to evaluate the performance.

Keywords

Container Terminal (CT); Performance Measurement System (PMS); Data Envelopment Analysis (DEA); Artificial Neural Network (ANN); Moth-Search Algorithm (MSA)

Hrčak ID:

247793

URI

https://hrcak.srce.hr/247793

Publication date:

21.12.2020.

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