The impact of import container flow characteristics on port operational efficiency

In this paper, we analyze different scenarios for container flows arriving at marine terminals to different destinations in the hinterland. The aim of the study is to verify how the type of import containers — standard, hazardous, and refrigerated — and their size affect the operational efficiency of the terminal. Relevant performance indicators, such as container dwell time, average and maximum number of waiting containers, and equipment utilization rate, are evaluated. To this end, we present a discrete-event simulation study that, although generalizable to any port, refers to a terminal in the port network of Genoa (Italy). The number of considered scenarios, illustrated in this paper, are taken from a synthetic data generator for logistics flows and used in Witness Horizon v.24 simulation software environment to execute independent runs at a steady state condition. To the authors’ knowledge, this is the first time that a sensitivity analysis based on the variation in the types of containers is presented. The performed simulation experiments can be of great interest to various port stakeholders. Indeed, the results show that the percentage composition of the type of import container over the annual time horizon considered has an impact on the indicators under analysis, favoring a more balanced distribution. However, again in relation to the same indicators, the variation in container size appears to be negligible. The study highlights how advance knowledge of the type of import containers can support port terminal management in terms of efficient management and optimization of resources, providing specific advice on the operational decisions concerning equipment and block yard allocation. • For the first time the impact of import container flow characteristics on port terminal efficiency. • An adaptable discrete-event simulation model for the analysis of container terminals is developed. • The role of integrating simulations and synthetic data of logistics flow is highlighted. • Findings reveal that the composition of container types affects relevant indicators. • Advance knowledge of the type of containers can provide useful information to decision makers .