A hybrid framework for evaluating the performance of port container terminal operations

IF 0.5 Q4 TRANSPORTATION
Mouhsene Fri, K. Douaioui, Nabil Lamii, C. Mabrouki, E. Semma
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引用次数: 4

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.
港口集装箱码头运营绩效评价的混合框架
本工作旨在将人工神经网络(ANN)和数据包络分析(DEA)集成在一个单一的框架中,以评估集装箱码头的运营绩效。拟议的框架基于三个步骤。在第一步中,提出了确定绩效指标的目标和影响系统的指标。在第二步中,通过使用Charnes Cooper和Rhodes(CCR)模型(定向输入)来计算系统的效率分数。在最后一步中,采用Moth搜索算法(MSA)作为一种新的方法来训练前馈神经网络(FNN)以确定效率分数。为了证明所提出的框架的有效性,采用丹吉尔和卡萨布兰卡两个集装箱码头来评估其性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.50
自引率
0.00%
发文量
19
审稿时长
8 weeks
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