Smart and sustainable logistics of Port cities: A framework for comprehending enabling factors, domains and goals

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.

Emerging approaches applied to maritime transport research: Past and future

Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision

Maritime transportation is the backbone of global supply chain. To improve maritime safety, protect the marine environment, and set out seafarers’ rights, port state control (PSC) empowers ports to inspect foreign visiting ships to verify them comply with various international conventions. One critical issue faced by the port states is how to optimally allocate the limited inspection resources for inspecting the visiting ships. To address this issue, this study first develops a state-of-the-art XGBoost model to accurately predict ship deficiency number considering ship generic factors, dynamic factors, and inspection historical factors. Particularly, the XGBoost model takes shipping domain knowledge regarding ship flag, recognized organization, and company performance into account to improve model performance and prediction fairness (e.g., for two ships that are different only in their flag performances, the one with a better flag performance should be predicted to have a better condition than the other). Based on the predictions, a PSC officer (PSCO) scheduling model is proposed to help the maritime authorities optimally allocate inspection resources. Considering that a PSCO can inspect at most four ships in a day, we further propose and incorporate the concepts of inspection template and un-dominated inspection template in the optimization models to reduce problem size as well as improve computation efficiency and model flexibility. Numerical experiments show that the proposed PSCO scheduling model with the predictions of XGBoost as the input is more than 20% better than the current inspection scheme at ports regarding the number of deficiencies detected. In addition, the gap between the proposed model and the model under perfect-forecast policy is only about 8% regarding the number of deficiencies detected. Extensive sensitivity experiments show that the proposed PSCO scheduling model has stable performance and is always better than the current model adopted at ports.

Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control

Port state control (PSC) inspection acts as a safeguard against maritime accidents and marine environment pollution. Due to limited inspection resources and high inspection costs, port states can o...

Ship selection in port state control: status and perspectives

An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities

To reduce the extra work, the operation cost, and the risk of cargo delay induced by the unloading of wrong containers, this study first develops a container color detection model to predict the color of the container being unloaded. The prediction results are then used to develop two crane operator alarm methods. Method 1 alerts the crane operator if the detected color of a container is not in compliance with the correct container color. Method 2 constructs a decision problem to decide whether to alert the operator. The results of numerical experiments show that methods 1 and 2 are better than the benchmark. Specifically, method 1 can save the expected annual total cost by about 82% while method 2 can save the expected annual total cost by about 85%. Extensive sensitivity analysis is also conducted to verify the methods performance and robustness. 

Development of computer vision informed container crane operator alarm methods

Regulations for the control of air-pollutant emissions from ships within pollutant emission control areas (ECAs) have been issued for several years, but the lack of practical technologies and fundamental theory in the implementation process remains a challenge. In this study, we designed a model to calculate the nitrogen-oxide-emission intensity of ships and the sulfur content of ship fuels using theoretical deduction from the law of the conservation of mass. The reliability and availability of the derived results were empirically evaluated using measurement data for NOx, SO2, and CO2 in the exhaust gas of a demonstration ship in practice. By examining the model and the measured or registered fuel-oil-consumption rates of ships, a compliance-determination workflow for NOx-emission intensity and fuel-sulfur-content monitoring and supervision in on-voyage ships were proposed. The results showed that the ship fuel’s NOx-emission intensity and sulfur content can be evaluated by monitoring the exhaust-gas composition online and used to assist in maritime monitoring and the supervision of pollutant emissions from ships. It is recommended that uncertainties regarding sulfur content should be considered within 15% during monitoring and supervision. The established model and workflow can assist in maritime monitoring. Meanwhile, all related governments and industry-management departments are advised to actively lead the development of monitoring and supervision technology for ship-air-pollutant control in ECAs, as well as strengthening the quality management of ships’ static data.

https://www.mdpi.com/2073-4433/14/1/175/pdf?version=1673602145

Chuansheng Peng

Papers

An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities

Ship selection in port state control: status and perspectives

Development of computer vision informed container crane operator alarm methods

A Study on Monitoring and Supervision of Ship Nitrogen-Oxide Emissions and Fuel-Sulfur-Content Compliance