Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts.

Despite the significant fluctuations in global trend due to the rising trade friction and the COVID19 pandemic, the container terminals are continuously working in three technology areas including automation, electrification and digitalization. This study reviewed recent technology trends as well as relevant research topics related to the container terminals, and investigated how the trends and topics would facilitate the terminals to achieve their strategic objectives. We also studied the trends in the container terminal industry before and after the pandemic outbreak. Recent progress shows that generally the long-term plans remain unchanged while there are some changes in timeline and priorities. The findings suggest that despite the common interest in long-term plans, gaps are still identified between academia and industry interests. Future directions are discussed for these technology areas, particularly in the context of the post-pandemic world, where the limited resources should be invested to the most urgent areas.

Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application.

Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity.

Network science approach to modelling the topology and robustness of supply chain networks: a review and perspective.

Due to the increasingly complex and interconnected nature of global supply chain networks (SCNs), a recent strand of research has applied network science methods to model SCN growth and subsequently analyse various topological features, such as robustness. This paper provides: (1) a comprehensive review of the methodologies adopted in literature for modelling the topology and robustness of SCNs; (2) a summary of topological features of the real world SCNs, as reported in various data driven studies; and (3) a discussion on the limitations of existing network growth models to realistically represent the observed topological characteristics of SCNs. Finally, a novel perspective is proposed to mimic the SCN topologies reported in empirical studies, through fitness based generative network models.

The kinematic features of motorcycles in congested urban networks.

The aim of this paper is to compare the kinematic features of motorcycles with those of passenger cars in urban traffic. The hypothesis that motorcycles' capability to swerve in urban traffic contributes to their seemingly assertive behaviour is examined. Data for this study were collected in afternoon peak hours at Central London using video recorders. Detailed information on the trajectories of 2109 vehicles (including 477 motorcycles and 1293 passenger cars) was extracted from the video images and the observable kinematic features were analysed. In addition, a model describing the longitudinal following behaviour of motorcycles was employed to analyse the impacts of motorcycles' swerving behaviour. The model was calibrated using Markov Chain Monte Carlo (MCMC) numerical methods. The observable kinematic features show that in comparison to passenger cars, motorcycles have shorter safety gaps, higher speeds and severer acceleration and deceleration rates reflecting their generally much higher power to weight ratios and usage of available braking power. However, the data also support the hypothesis that motorcyclists maintain a considerable safety margin as they have the ability to avoid a collision by swerving away.