Application of Machine Learning Algorithms for Volumetric Optimization and Cargo Consolidation in Import Containers to the USA
Application of Machine Learning Algorithms for Volumetric Optimization and Cargo Consolidation in Import Containers to the USA
DOI:
https://doi.org/10.51473/rcmos.v1i1.2023.1396Keywords:
Machine Learning; international logistics; cargo consolidation; volumetric optimization; import.Abstract
The growing logistical complexity in international trade demands increasingly sophisticated solutions to achieve efficient resource utilization and cost reduction. In this context, the application of Machine Learning (ML) algorithms in volumetric optimization and cargo consolidation in import containers to the United States represents an innovation with transformative potential. This article investigates how supervised and unsupervised learning techniques can be applied to predict occupancy patterns, improve space utilization, and minimize logistical waste. Furthermore, it discusses the integration of these technologies into transportation management platforms, analyzing their impact on business competitiveness, sustainability, and the reduction of operational bottlenecks.
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References
BALLINI, Fabio; BOCCARDI, Guido. Sustainable Transport and Logistics: A Framework for Machine Learning Applications. Journal of Shipping and Trade, v. 5, n. 4, p. 22-39, 2020.
BROWNE, Michael; ALLEN, Julian; ANDERSON, Stephen. Urban Logistics and Freight Transport. New York: Routledge, 2019.
CHOPRA, Sunil; MEINDL, Peter. Supply Chain Management: Strategy, Planning, and Operation. 7. ed. Boston: Pearson, 2021.
CHRISTOPHER, Martin. Logistics & Supply Chain Management. 5. ed. Harlow: Pearson Education, 2016.
KELLY, George; MURPHY, Paul. Containerization and International Trade Efficiency: The Role of Technology. International Journal of Logistics Research, v. 12, n. 2, p. 155-172, 2019.
MITCHELL, Tom. Machine Learning. New York: McGraw-Hill, 1997.
MONTGOMERY, Douglas C.; RUNGER, George C. Applied Statistics and Probability for Engineers. 7. ed. Hoboken: Wiley, 2019.
RUSSELL, Stuart; NORVIG, Peter. Artificial Intelligence: A Modern Approach. 3. ed. Upper Saddle River: Pearson, 2010.
SILVA, João Carlos; OLIVEIRA, Renato. Inteligência Artificial Aplicada à Logística Internacional: Perspectivas e Desafios. Revista de Comércio Exterior, v. 35, n. 2, p. 67-84, 2020.
UNCTAD. Review of Maritime Transport 2020. Geneva: United Nations Conference on Trade and Development, 2020.
ZHANG, Rui; ZHAO, Ling. Machine Learning for Logistics Optimization: Case Studies in Containerized Transport. Journal of Transport and Supply Chain Management, v. 14, n. 3, p. 44-61, 2020.
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Copyright (c) 2023 Ivan de Matos (Autor)
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