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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 • 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]