Machine Learning Models for Cost-Effective Shipping Line Selection: A Comparative Analysis for Freight Forwarders | ||||
| المجلة العلمية للدراسات التجارية والبيئية | ||||
| Article 46, Volume 15, Issue 4, October 2024, Page 1992-2013 PDF (423.58 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/jces.2024.414468 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Muhammad Aref Arfeen* 1; Hatem Abdelkader* 2; Nermine Khalifa* 3 | ||||
| 1College of International Transport and Logistics, Arab Academy for Science, Technology and Maritime Transport, Aswan, Egypt, | ||||
| 2Information System Department Faculty of Computers & Information Menoufia University, Menoufia, Egypt. | ||||
| 3Business Information Systems Department, College of Management and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt, | ||||
| Abstract | ||||
| The effectiveness of machine learning models in making cost-effective shipping line selections is investigated in this study from the perspective of freight forwarders. We had access to a dataset encompassing 983 shipment records from 37 different Egyptian freight forwarding companies. We then tested three different machine learning algorithms to see which one best predicted cost-effective shipping selections. The three algorithms were: Decision Trees, K-Nearest Neighbors (KNN), and Naive Bayes. After thoroughly testing these three algorithms, we determined that the best algorithm for use with our dataset, and the best one for use broadly within the market, was the Decision Tree method. | ||||
| Keywords | ||||
| Freight Forwarding; Machine Learning; Decision Trees; Shipping Line Selection; Logistics Optimization; Cost-Effectiveness; Predictive Analytics | ||||
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