Table 11 Overview of the literature adhering to the AI technologies in port and shipping industries
Author (year) | Publication | Challenge addressed | Studied port | Scope of study |
|---|---|---|---|---|
Parolas (2016) | ETA prediction for containerships at the Port of Rotterdam using Machine Learning Techniques | ETA | Rotterdam (Netherlands) | AI Advantages |
Flapper (2020) | ETA Prediction for Vessels using Machine Learning | ETA | Rotterdam (Netherlands) | AI Advantages |
Moscoso-López et al. (2021) | A machine learning-based forecasting system of perishable cargo flow in maritime transport | Prediction of cargo flow | Algeciras (Spain) | AI Advantages |
Ansorena and Ansorena (2020) | Managing uncertainty in ferry terminals: a machine learning approach | Congestion | Ceuta (Spain) | AI Advantages |
Viellechner and Spinler (2020) | Novel Data Analytics Meets Conventional Container Shipping: Predicting Delays by Comparing Various Machine Learning Algorithms | Congestion | No port mentioned | AI Advantages |
Cammin et al. (2020) | Applications of Real-Time Data to Reduce Air Emissions in Maritime Ports | Emission, ETA | Hamburg (Germany) | AI Advantages |
Martins et al. (2020) | A Dynamic Port Congestion Indicator—A Case Study of the Port of Rio de Janeiro | Congestion | Rio de Janeiro (Brazil) | AI Advantages |
Atak et al. (2021) | Container Terminal Workload Modeling Using Machine Learning Techniques | Quay Crane planning | No port mentioned (Turkey) | AI Advantages |
Chargui et al. (2021) | A quay crane productivity predictive model for building accurate quay crane schedules | Quay Crane planning | No port mentioned | AI Advantages |
Yang and Chang (2020) | Forecasting the Demand for Container Throughput Using a Mixed-Precision Neural Architecture Based on CNN–LSTM | Predicting Container's Demand | No port mentioned (Taiwan) | AI Advantages |
Darendeli et al. (2021) | Container Demand Forecasting Using Machine Learning Methods: A Real Case Study from Turkey | Predicting Container's Demand | Mersin (Turkey) | AI Advantages |
Luo and Huang (2020) | Port Short-term Truck Flow Forecasting Model Based on Wavelet Neural Network | Congestion, Truck Flow forecasting | Guangzhou (China) | AI Advantages |
Kunnapapdeelert and Thepmongkorn (2020) | Thailand port throughput prediction via particle swarm optimization based neural network | Port throughput forecasting | Bangkok (Thailand) | AI Advantages |
Wang et al. (2018) | A Forecast Model of the Number of Containers for Containership Voyage | Predicting container volume | No port mentioned | AI Advantages |
Shen et al. (2017) | A deep Q-learning network for ship stowage planning problem | Ship stowage | Ningbo (China) | AI Advantages |
Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN) | Ship Queuing | Tanjung Pelepas (Malaysia) | AI Advantages | |
Gao et al. (2018) | Deep learning with long short-term memory recurrent neural network for daily container volumes of storage yard predictions in port | Yard equipment Planning | No port mentioned | AI Advantages |
El Mekkaoui et al. (2020) | A Way Toward Low-Carbon Shipping: Improving Port Operations Planning using Machine Learning | Low-Carbon Shipping | North African (Morocco,) | AI Advantages |
Oucheikh et al. (2021) | Rolling Cargo Management Using a Deep Reinforcement Learning Approach | Cargo Management | No port mentioned | AI Advantages |
Adi et al. (2020) | Interterminal Truck Routing Optimization Using Deep Reinforcement Learning | Yard Truck planning | Busan (South korea) | AI Advantages |
Kourounioti et al. (2016) | Development of models predicting the Dwell Time of containers in port container terminals | Dwell Time forecasting | No port mentioned | AI Advantages, AI barriers |
Mi et al. (2019) | Research on regional clustering and two-stage SVM method for container truck recognition | Container recognition | Taicang (China) | AI Advantages, AI barriers |
de León et al. (2017) | A Machine Learning-based system for berth scheduling at bulk terminals | Berth assignment | No port mentioned | AI Advantages |
Gao et al. (2019) | The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network | Yard block forecasting | No port mentioned | AI Advantages |
Zhang et al. (2020) | Machine learning-driven algorithms for the container relocation problem | Container relocation planning | No port mentioned | AI Advantages |
Garrido et al. (2020) | Predicting the Future Capacity and Dimensions of Container Ships | Capacity prediction | Barcelona (Spain) | AI Advantages |
Zhang et al. (2020) | Motion Planning Using Reinforcement Learning Method for Underactuated Ship Berthing | Ship Berthing | No port mentioned | AI Advantages |
Lee et al. (2020) | Development of Machine Learning Strategy for Predicting the Risk Range of Ship's Berthing Velocity | Control Berthing Risk | No port mentioned | AI Advantages |
Niestadt et al. (2019) | Artificial intelligence in transport Current and future developments, opportunities and challenges | AI in road transport, aviation, railway transport shipping, navigation and ports | No port mentioned | AI Advantages, AI barriers |
Alop (2019) | The Main Challenges and Barriers to the Successful "Smart Shipping" | AI in smart shipping | No port mentioned | AI Advantages, AI barriers |
Babica et al. (2019) | Digitalization in Maritime Industry: Prospects and Pitfalls | AI in maritime industry | No port mentioned | AI Advantages |
Stepec et al. (2020) | Machine Learning based System for Vessel Turnaround Time Prediction | Vessel's Turnaround time prediction | Bordeaux (France) | AI Advantages, AI barriers |
Xie et al. (2017) | Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology | Container throughput prediction | Singapore (Singapore), Los Angeles (USA) | AI Advantages |
Yan et al. (2021) | An Artificial Intelligence Model Considering Data Imbalance for Ship Selection in Port State Control Based on Detention Probabilities | Ship detention | Hong Kong (China) | AI Advantages |