Scheduling, Berthing, and Cargo Handling Optimization Using Queuing Theory and Deep Learning
DOI:
https://doi.org/10.29070/g7h40532Keywords:
berthing, cargo handling, queuing models, deep learning, scheduling, maritime transportationAbstract
Maritime transportation is very important for global trade as it is responsible for 80% of movements of goods across the world. Considering the increase in freight movements, efficient system is needed for cargo handling and scheduling at ports. The existing “first-come-first-serve (FCFS)” approach is incapable to ensure operational efficiency under complex situations like parallel scheduling with various cargo setups. Data-driven strategies are much needed, given the rising demand. Robust berth scheduling is needed for conflict-free queuing of vessels in terminal, given the actual vessel arrival uncertainty, which may be caused due to sea current and cross wind.
Cargo handling is an important process in maritime logistics. Decisions like selecting proper equipment, type of ownership (outsourcing or in-house), and capacity to operation-based decisions like scheduling, resource allocation, and routing are important for efficiency of cargo handling systems. Different tools and approaches are used by industry experts to determine these handling systems to choose the best policies. This study explores previous works related to optimization and evaluation of cargo handling systems with queuing models. In addition, this study conducts comprehensive analysis through systematic literature review. It provides thorough understanding to industry practitioners and research scholars about queuing networks and deep learning methods used for berthing optimization.
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