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• Develop a novel data-driven framework for port-skipping identification. • Empirically explore decision preferences of shipping lines for port-skipping. • Reveal various strategic decisions of different shipping lines to heal service disruptions. • The proposed methodology can be cross-applied to identify other disruption recovery behaviors. Service disruption hurts the reliability of liner shipping schedules and the lack of high-quality data impedes research on schedule recovery in liner shipping. With the support of the Automatic Identification System (AIS), a satellite-based tracking system acquiring real-time records of vessels' navigation trajectories worldwide, this study develops a novel data-driven framework to uncover the vessel schedule disruption recovery behavior (i.e., port-skipping). The framework consists of a series of independent yet closely related algorithms, which are in turn tasked with screening port calls, estimating closed-loop routes, measuring similarity between different routes, and identifying port-skipping behavior, respectively. Compared with partial proprietary data from the shipping industry and port authorities, the identification results can provide transparent datasets with wide applications and much easier access publicly. The developed data-driven framework is implemented with vessel trajectory information of around 2,000 container vessels worldwide from January 2016 to December 2020, and the results prove its validity and practical value for liner shipping scheduling management. [ABSTRACT FROM AUTHOR]

Highlights • We optimize shipping schedule for steel plants cluster alongside inland river. • The model considers timely demands of steel plants over multiple periods. • A MIP formulation and dynamic programming algorithm are developed. • We explore the iron ore trade volume under various scenarios. Abstract This paper aims to provide an optimal shipping schedule for steel-manufacturing plants cluster alongside the Yangtze River by developing a mixed integer programming formulation. The model takes into account the multi-layer shipping network on the Yangtze River, the dynamic iron ore price and transportation costs, and the timely demands of steel plants over multiple periods. By applying the proposed model to a real-world case, several interesting findings are observed, leading to further useful managerial insights. We also propose an exact solution approach based on a dynamic programming algorithm to a special case of this problem motivated by real-world practice. [ABSTRACT FROM AUTHOR]

Highlights • A new, significant, and timely topic and consider inland New Eurasia Land Bridge. • New bi-level programming model and exact reformulation and solution approach. • Utilize real data in the study and thus provide more practical managerial insights. Abstract This paper aims to reconstruct the shipping service network between Asia and Europe by considering the improvement of New Eurasian Land Bridge rail services and Budapest-Piraeus railway. In particular, to reflect the decision making process in reality, a bi-level programming model is established to maximize the total profit of the liner shipping company in the upper level and meanwhile to minimize the total cost of the shippers in the lower level. Computational experiments considering different scenarios are conducted to obtain the new optimal networks under different cases. Several insightful findings are observed, further leading to useful managerial insights. [ABSTRACT FROM AUTHOR]

• Propose a standardized method to estimate port turnover rate based on satellite data of ocean ships. • Construct berth polygon areas for terminals based on Greatmaps (GMap) visual technique. • Design two tailor-made algorithms to estimate the berthing time of ship in a berthing event. • Assess port turnover rate with aggregate berthing time at a port and its historical port throughput. • Use calculated turnover rate to estimate the monthly throughput of global ports. Accurate assessment of port turnover rate is essential for port operators and shipping carriers to benchmark and improve their operations. This study proposes a standardized method to estimate the port turnover rate based on satellite data of ocean ships. This method can be generalized to accommodate ports of different geographic and operational characteristics with minimum input and running times. To achieve the research objective, we first construct berth polygon areas for terminals based on Greatmaps (GMap) visual technique. Then, two tailor-made algorithms are proposed to estimate the berthing time of ship in a berthing event. Finally, we assess the port turnover rate with aggregate berthing time at a port and its historical port throughput. Assuming that the turnover rate is unchanged in the short term, we can use the estimated turnover to estimate the monthly throughput of global ports. The findings suggest the average Mean Absolute Percentage Error (MAPE) of our estimation is 3.84%. Standardized and high-frequency port statistics are highly valued by the industry but very costly to access. The proposed method makes high-frequency port turnover rate and throughput available for a wide range of users. The statistics and findings will enhance standardization and transparency of port statistics and promote the sustainable development of port industry. [ABSTRACT FROM AUTHOR]

To handle the product configuration problem with uncertain supply and demand, stochastic programming approach is applied to formulate the problem as a stochastic mixed-integer programming model. Carbon emission is further integrated into the deployed stochastic model under four different carbon emission regulations. Benders decomposition algorithm is utilized to solve the stochastic model. Computational studies show that the Benders decomposition method can solve large-scale stochastic programming problems with faster convergence rate than commercial solver CPLEX does. The results from the numerical experimental analysis demonstrate the impacts of carbon emission regulations on product configuration decisions. [ABSTRACT FROM AUTHOR]

• We explore the reasons behind the wrong destination reports in AIS. • We use data mining techniques to extract information from AIS. • We build a discrete choice model to analyse the reliability of observed reports. • Our model has an 84.1% accuracy rate in ascertaining the correctness of destination reports. • We provide managerial and policy suggestions to ship operators, port authorities, and regulators. The Automatic Identification System (AIS) is a shipping database that includes the physical characteristics and real-time dynamics of ships. It has attracted great attention from academia recently and has been broadly applied in solving various problems in different fields. The voyage destination report is a piece of information recorded in AIS that indicates the heading port in a ship's voyage. This information is widely referred to by port operators for traffic estimation, and by shipping traders for supply forecasting, etc. However, we find that a considerable proportion (nearly 40%) of this information has been erroneously entered, both intentionally and unintentionally. In this paper, we aim to propose targeted policies to correct the inaccurate reports based on assessing the probability of observing wrong destination port reports of ships in AIS. To this end, we first of all conduct extensive interviews with relevant shipping stakeholders to understand the reasons behind the wrong destination reports. Second, based on the interviews and relevant literature we propose the influence factors. Third, we generate a data sample set based on the voyages performed by Capesize and Panamax bulk ships around the globe in a year. To generate this sample set, we leverage data mining techniques to extract the information from an AIS database and other databases. Finally, a discrete choice model is built to achieve the proposed objective. The results demonstrate that our model has an 84.1% accuracy rate in ascertaining the correctness of destination reports observed in AIS. We also find that, for a voyage, the speed of the ship, the historical accuracy rate of destination reports made by the ship, and the distance between the recognized origin and the reported destination of the voyage, have the most significant impacts on the accuracy of the destination report. Based on the findings, we provide managerial and policy suggestions to ship operators, port authorities, and regulators. [ABSTRACT FROM AUTHOR]

Whether shipping market affects international iron ore trade arouses a lot of discussions in recent years. To evaluate the impact of shipping, we build a mixed complementarity-based equilibrium model to understand the strategic behavior and interaction among major players, including importers, exporters, and carriers. Different from models found in other resource trades, this one considers the heterogeneity of iron ores and the production capacity allocation of exporters. In this three-party equilibrium model, importers and exporters compete in a Cournot fashion, incorporating the endogenous freight rate derived from a carrier module. This model is an instance of Discretely Constrained Mixed Complementarity Problem (DC-MCP), for which we provide a solution procedure based on Mixed Integer Nonlinear Program (MINLP) reformulation and convexification techniques. Our model performs better compared with those with given shipping cost and production capacity constraint. In addition, we apply the model to analyze the impact of shipping on the iron ore international trade through a numerical study. The results show that shipping can slightly dampen the negative impact on iron ore trade volume from an unexpected reduction in importer budget. For different exporters, this dampening effect varies by freight rate. [ABSTRACT FROM AUTHOR]

• We study the barge evacuation planning problem that arises in real situations. • We present a Mixed Integer Programming model for the problem and prove that the problem is strongly NP-hard. • We propose a tailored heuristic algorithm to solve the problem. • Computational experiments show that our proposed method is effective and efficient. This paper investigates a barge evacuation planning problem (BEPP) that can arise during land reclamation projects. The problem was motivated by the issue faced in actual practice by the Hong Kong International Airport (HKIA). In this problem, a fleet of heterogeneous barges working at an offshore land reclamation site needs to be evacuated to coastal shelters prior to the arrival of a storm. Having no propulsion power of their own, these barges must be towed by tug boats in order to be evacuated. The problem under consideration is very complicated since it involves a series of inter-correlated assignment and scheduling decisions at different planning levels. To solve the problem, this paper first formulates the problem as a nonlinear Mixed Integer Programming (MIP) model. The model is then linearized. We further proved that the BEPP is NP-hard in the strong sense. In view of the complexity of the problem, a tailored heuristic method is proposed. Extensive numerical experiments and a case study are performed, and the results demonstrate the effectiveness and efficiency of the solution method. Land reclamation projects have become increasingly popular in recent years, and the proposed method is applicable to solving BEPPs arising in similar scenarios. [ABSTRACT FROM AUTHOR]

• Empirically explore the factors that affect shipowners' energy choices. • Apply various data mining methods and a threshold discrete choice model. • Address the heterogeneity of the carriers towards environmental awareness. • Provide important practical implications for responding to the new emissions regulations. On January 1st, 2020, the International Maritime Organization (IMO) implemented a new regulation for a 0.50% global sulphur cap for marine fuels, which was a dramatic decrease from the previous emissions cap of 3.5%. The new regulation will have an enormous impact on the shipping market. At present, there are different feasible schemes for reducing sulphur emissions from ships. Shipowners need to consider the economic cost, energy feasibility, and other relevant factors of different schemes before making decisions. This paper empirically explores the factors that affect shipowners' energy choices. Based on the new emerging individual ship dynamic data, Automatic Identification System (AIS), and other relevant databases, we apply various data mining methods and a threshold discrete choice model combined with an oversampling technique to conduct quantitative measurements and statistical analyses of factors for each ship type that affect the shipowners' choices. Three groups of indicators, including ship characteristics, shipowner characteristics, and market conditions, are considered in our analysis. In the model, we also address the heterogeneity of the carriers towards environmental awareness. This study provides important practical implications for responding to the new emissions regulations among maritime and maritime-related industries and policymakers. [ABSTRACT FROM AUTHOR]