Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
In recent years, there has been a growing research interest in integrating machine learning
techniques into meta-heuristics for solving combinatorial optimization problems. This …
techniques into meta-heuristics for solving combinatorial optimization problems. This …
Applications of machine learning methods in port operations–A systematic literature review
Ports are pivotal nodes in supply chain and transportation networks, in which most of the
existing data remain underutilized. Machine learning methods are versatile tools to utilize …
existing data remain underutilized. Machine learning methods are versatile tools to utilize …
[HTML][HTML] The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review
A significant recent technological development concerns the automation of knowledge and
service work as a result of advances in Artificial Intelligence (AI) and its sub-fields. We use …
service work as a result of advances in Artificial Intelligence (AI) and its sub-fields. We use …
[HTML][HTML] Smart technologies and port operations: Optimal adoption strategy with network externality consideration
Smart technologies that streamline information among stakeholders can enhance
operational efficiency at sea ports. The benefit of network externality from smart technologies …
operational efficiency at sea ports. The benefit of network externality from smart technologies …
[HTML][HTML] The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions
Abstract Container Terminals (CTs) are continuously presented with highly interrelated,
complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in …
complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in …
A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem
J García, C Maureira - Applied Soft Computing, 2021 - Elsevier
Optimization algorithms and particularly metaheuristics are constantly improved with the
goal of reducing execution times, increasing the quality of solutions, and addressing larger …
goal of reducing execution times, increasing the quality of solutions, and addressing larger …
A decision tree model for the prediction of the stay time of ships in Brazilian ports
LR Abreu, ISF Maciel, JS Alves, LC Braga… - … Applications of Artificial …, 2023 - Elsevier
Maritime transport is an alternative modal logistic in transporting cargo for long distances
and in large quantities. However, the logistical planning for this modal becomes costly due …
and in large quantities. However, the logistical planning for this modal becomes costly due …
Robust berth scheduling using machine learning for vessel arrival time prediction
In this work, the potentials of data-driven optimization for the well-known berth allocation
problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel …
problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel …
Integrated lot-sizing and scheduling: Mitigation of uncertainty in demand and processing time by machine learning
Production rescheduling is one of the most challenging problems in production
management, in which some parameters, such as customer demand and job processing …
management, in which some parameters, such as customer demand and job processing …
A reduced vns based approach for the dynamic continuous berth allocation problem in bulk terminals with tidal constraints
N Cheimanoff, F Fontane, MN Kitri… - Expert Systems with …, 2021 - Elsevier
This paper deals with the problem of allocating berth positions for vessels in export bulk port
terminals considering tidal constraints and was first formulated by Ernst et al.,(2017). This …
terminals considering tidal constraints and was first formulated by Ernst et al.,(2017). This …