This paper presents an optimised approach to scheduling tugboats for an outsourced port company in Europe's second-largest seaport. Using allowed data from 2022, we developed a one-day operational scenario for harbour activities. By implementing an integer programming model with OR-Tools, tugboat operators can improve their scheduling processes, ensuring more efficient resource utilisation. We recommend adopting a mathematical model to address the job-shop scheduling problem, considering the critical factors relevant to Belgian harbour operations. A genetic algorithm was also employed as an alternative method to solve the issue under the specific conditions studied. The proposed model provides the port with a structured process to meet its operational needs. These methods demonstrate that tugboat scheduling decisions can be optimised efficiently and may serve as a reference for other ports and organisations facing similar operational challenges.
In line with the importance of global trade and environmental sustainability, sustainable logistics performance (SLP) has become a research topic in academia and practice. Logistics operations support economic growth, yet their environmental impacts remain a concern. This study examines the integration of sustainability principles into logistics from environmental, economic and operational perspectives. To address the knowledge gap, it develops an approach for criterion weighting and country rankings in SLP measurement. Using hybrid fuzzy and grey multi-criteria decision making (MCDM) methods, the study evaluates the SLP of European Union (EU) countries using nine years of Eurostat data. A hybrid MCDM model combining fuzzy DEMATEL and ARAS-G is proposed. The analysis evaluates the SLP of 26 EU countries across five criteria and identifies the Netherlands, Estonia, France, Italy, and Spain as the top-performing countries. A sensitivity analysis was conducted to assess the model's reliability, confirming its robustness and consistency in the evaluation results.
This study investigates how port throughput scale and throughput growth relate to sulphur-oxides (SOX) concentrations at 18 major coastal ports in China. Using a port-year panel dataset over 2005-2019 and combining satellite-based SOX observations with a consistent set of port operational indicators, we apply quantile regression to examine nonlinearities and distributional heterogeneity. The results suggest an EKC-type pattern in which expanding throughput can increase SO(X )in earlier stages, whereas further expansion can coincide with reduced SOX as ports modernise and compliance capacity strengthens. We also document pronounced regional and scale heterogeneity: northern and eastern ports exhibit an inverted-U relationship between throughput and SOX, while southern ports show a U-shaped pattern. Overall, high-throughput ports tend to exert stronger impacts on SOX outcomes, underscoring the role of operational intensity. These findings imply that mitigation policies should be tailored to port-specific characteristics, including development stage, scale and regional context.
This study investigates the dry port location problem in the context of sequential seaport competition and environmental concerns. A bi-level programming model is proposed: the upper level aims to maximise the leader seaport's profit and minimise carbon emissions from transportation routes, while the lower level seeks to maximise the cargo flow of the follower seaport. A bi-level programming algorithm incorporating a heuristic approach (BLPAIHA) is designed to solve the model. The model and algorithm are validated through case studies of Beibu Gulf Port and Zhanjiang Port. The numerical results show that the optimal dry port location can balance economic and environmental objectives. Additionally, under lower values of environmental concern coefficients of seaports and shippers, location decisions are more driven by economic benefits, whereas under higher values of them, the preference shifts towards closer, low-carbon dry port locations. This study emphasises the importance of integrating economic and environmental goals in dry port location decisions to achieve sustainable development.
The logistics industry is one of the largest contributors to global CO2 emissions, making efficiency assessment under carbon constraints essential for sustainable development. This study evaluates the logistics efficiency of 30 Chinese provinces from 2015 to 2021 by explicitly incorporating CO2 as an undesirable output. The super-slack-based measure (super-SBM) model and the Malmquist productivity index are applied to measure static and dynamic efficiency and to decompose performance into scale efficiency, pure technical efficiency, efficiency change, and technological change. The results show that static efficiency declined by an average of 7.8% after accounting for carbon emissions, with the largest reductions observed in Shanghai (-0.441) and Zhejiang (-0.203). Dynamic efficiency decreased in 21 out of 30 provinces, mainly due to slower technological progress. In contrast, southern provinces exhibit greater resilience under carbon constraints. These findings provide quantitative evidence for region-specific decarbonisation strategies in the logistics sector.
This article combines panel data of 18 major Chinese port cities from 2011 to 2021. Partial least squares and coupled coordination degree models are used to explore the mechanism and relationship of digital economy on sustainable port development. Results show: 1) In terms of spatial-temporal evolution, the digital economy generally shown a stable growth trend, with significant differences between cities. In terms of dimensional evolution, developmental stages are digital infrastructure, industrial digitisation, and synergistic development of industrial digitisation and digital industrialisation; 2) the sustainable development of ports shows a fluctuating trend, with differences in levels and a 'Matthew effect'. Sustainable development levels are concentrated in the low, medium-low, and medium levels; 3) the impact of urban digital economy on port sustainable development involves three stages: port-driven, city-driven, and dual-driven, with high coupling between the two but relatively low coupling coordination degree.
Container terminal operations are rapidly adopting artificial intelligence (AI) technologies to improve efficiency, sustainability, and automation amid ongoing digital transformation. This study conducts a comprehensive bibliometric analysis of 391 publications (1992-2024) from the Web of Science Core Collection using Biblioshiny 4.0. The findings reveal a paradigm shift toward AI-driven optimisation across key areas, including berth allocation, crane scheduling, truck fleet management, and intelligent port automation. Despite this progress, a critical gap remains: the lack of empirical validation using real operational data. This gap highlights the urgent need for cross-industry collaboration to bridge theory and practice. The study urges policymakers and port authorities to implement standardised AI frameworks, invest in workforce upskilling, and enhance digital infrastructure. Future research should focus on scalable, data-validated AI applications and on promoting longitudinal case studies and industry partnerships to ensure the effective, sustainable integration of AI technologies into port logistics.
Various real-world engineering examples need to be settled by appropriate methods. However, many of them are proved as NP-hard problems with huge computational complexity, which causes premature convergence and slow computing efficiency. In this study, we are concerned with the combinatorial optimisation problems for optimal scheduling tasks in container terminals by utilising and improving the quantum-behaviour heuristic algorithm. First, an integrated two-stage model for the berth allocation, quay crane assignment, and quay crane scheduling problem (BACASP) in container terminals is presented to minimise the running costs in the given time horizon. To deal with the computation demand, a quantum-behaviour heuristic algorithm (QGA-E) with stronger global searching ability and higher computation efficiency is developed. The above works are certified to be feasible according to a series of experimental studies with datasets from the real container terminal.
This study focuses on predicting carbon emissions from China's waterborne freight transport and identifying peak times. A bottom-up model based on freight turnover is developed. After comparing three models, the long short-term memory (LSTM) network is used to forecast emissions from 2021 to 2040, and the Mann-Kendall test is applied to detect peaks. Results show inland, coastal, and oceanic transport will peak in 2030, 2032, and 2029, with overall waterborne emissions peaking around 2030 and declining significantly (P < 0.05). Validation confirms LSTM outperforms seasonal autoregressive integrated moving average (SARIMA) and Extreme Gradient Boosting (XGBoost). Furthermore, the study suggests that the promotion of clean energy sources, such as LNG and hydrogen, along with optimisation of energy infrastructure, could expedite the low-carbon transformation of waterborne transport. This paper offers methodological support for precise carbon emission measurement and peak time determination, providing practical reference value for China's achievement of its dual carbon goals.
This study explores the relationship between exchange rates and oil transportation costs, emphasising the interconnectedness between financial markets and the maritime industry. Using a connectedness approach based on the time-varying parameter vector autoregression (TVP-VAR) model, the study first examines how exchange rate fluctuations affect oil freight indices such as Baltic Dirty Tanker Index (BDTI) and the Baltic Clean Tanker Index (BCTI). It then investigates how tanker markets, in turn, transmit shocks to financial markets. The findings indicate that oil shipping costs are significantly influenced by currency volatility, while the tanker market also acts as a shock transmitter, particularly during economic crises such as the COVID-19 pandemic and geopolitical conflicts. These results provide critical insights for policymakers and investors in managing exchange rate risks and stabilising global trade and energy markets.
This paper shows that transshipment ports can be key contributors to enhancing trade relations and strategic port partnerships between the host country and the connected markets. The paper develops a forecasting model based on international trade dynamics between foreland markets and the world's major production centre, China, and validates with data sourced from the World Integrated Trade Solution and China's Trade Yearbook to determine the port throughput of a regional hub port (the Port of Colombo). The model's validity is confirmed through rigorous statistical tests and the findings revealed the Port of Colombo's long-term dependence on trade with foreland nations trading with China. The results support the need for the port's host country to adopt trade integration strategies for its sustained growth. The model significantly enhances the forecasting accuracy for transshipment hubs, offering valuable insights for policymakers and port authorities to promote sustainable growth through improved trade integration strategies.
This study investigates the structural characteristics of the global shipbreaking trade network using complex network analysis and bilateral trade data for 2014, 2017, 2020, and 2023. The analysis identifies Bangladesh, India, Turkey, and Pakistan as central nodes in the network, reflecting their role as major destinations for end-of-life vessels. Indicators such as indegree, outdegree, PageRank, authority, and modularity were employed to examine connectivity patterns, community structures, and network vulnerability. The results reveal a declining number of nodes and edges, indicating growing concentration of shipbreaking activities in a limited set of countries. Modularity analysis shows the emergence of regional clusters, particularly linking South Asian and Mediterranean hubs, while vulnerability tests confirm that the removal of highly central countries significantly weakens the network. These findings suggest that although shipbreaking supports resource recovery and cost efficiency, its concentration heightens risks related to sustainability, safety, and resilience. Overall, the study adds to the limited literature on shipbreaking and suggests that geographically diversifying ship recycling under harmonised environmental and labour standards could mitigate risks and support long-term sustainability.
In this study, we investigate the volatility spillover impact of oil prices on tanker freight rates, by calculating the monthly volatilities of eleven representative tanker freight indices, West Texas Intermediate (WTI), and Brent futures price indices to measure the oil and tanker shipping markets risk, respectively. In particular, the high-frequency component of the tanker freight rate volatility is disentangled by adopting the ensemble empirical mode decomposition (EEMD) method and Fine-to-coarse algorithm. Besides, the time-varying parameter vector autoregressive (TVP-VAR) model is employed to analyse the impact of crude oil futures prices on the high-frequency component of tanker freight rates at the volatility level. Our results reveal that crude oil price volatility affects tanker freight rate volatility on different routes to varying degrees, and crude oil price volatility has a positive spillover effect on tanker freight rate volatility on short-haul routes and small-sized ships located on routes.
