In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach.
PurposeA large part of maritime container supply chain costs is generated by carriers in port hinterland logistics. Carriers which operate in the hinterland are under pressure to reduce costs and increase profitability, and they face challenges of fierce price competition. This study aims to explore how collaboration is perceived and implemented by carriers in truck container logistics in the port hinterland as a way to tackle these issues.Design/methodology/approachThis study adopts a qualitative multiple case study approach. Qualitative interviews with carriers in the port hinterland of Hamburg, Germany, were conducted and analyzed using grounded theory.FindingsThe study reveals two collaboration types in the hinterland, based on the different carriers' interpretation of market conditions as changeable or as given, driving their collaboration mindsets and strategic actions: The developer, who has a proactive collaboration mindset and practices strategic maneuvers toward changing poor market conditions through collaboration, and the adapter, who has a defensive collaboration mindset and perceives market conditions as given and constraining collaboration.Research limitations/implicationsThe qualitative results will help researchers better understand how collaboration practices depend on the carriers' subjective interpretations and perceptions of the market.Practical implicationsBased on the findings, managers of carriers gain an understanding of the different types of actors in their market and the relevance of acknowledging these types. Consequently, they can design appropriate strategic measures toward collaboration.Originality/valueThe findings for the first time provide exploratory insights of carriers' mindsets.
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