2603. A Systematic Literature Review on Machine Learning-Enabled Optimization for Container Yard Management
Invited abstract in session TD-32: Container Stacking and Yard Planning-2, stream Maritime and Port Logistics.
Tuesday, 14:30-16:00Room: Maurice Keyworth 1.09
Authors (first author is the speaker)
| 1. | Nazin Shokravi
|
| OPERATIONS & SUPPLY CHAIN MANAGEMENT, University of Liverpool | |
| 2. | Dongping Song
|
| School of Management, University of Liverpool | |
| 3. | Yuanjun Feng
|
| Operations & Supply Chain Management, University of Liverpool |
Abstract
Abstract: Effective Container Yard Management (CYM) is a critical component of global maritime logistics, directly influencing port efficiency, supply chain resilience, and operational costs. In the recent years, machine learning (ML) has emerged as a transformative tool to optimize CYM processes. This paper presents a systematic literature review on Machine Learning-enabled optimization techniques applied to container yard management, synthesizing advancements, challenges, and future research directions. Following the PRISMA framework, we reviewed 461 peer-reviewed articles based on the Scopus database. The thematical analysis is conducted from multiple perspectives, including application areas, types of ML models, types of optimization methods, and the performance measures. This study provides researchers and practitioners with a comprehensive view of ML applications in CYM, equips researchers with insights to advance data-driven yard management systems, and provides further research opportunities that are needed in the era of smart ports.
Keywords
- Machine Learning
- Optimization Modeling
- Analytics and Data Science
Status: accepted
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