Keywords
Container Terminals
Productivity
Operations Research
Container Data
How to Cite
Abstract
Terminals rely on optimization tools used on merchandise location, quay occupation or vehicle trajectories, in order to minimize the movements together with the time dedicated to every task. However, operations are developed into an environment that induces variability to the theoretical model used to schedule and control the operations. Given the complexity of the port operations, the artificial intelligence systems are positioned as a good choice to analyze such processes. In the near future terminals, where automation is set out as an extended reality, monitoring of operational variables carried out offers great possibilities to make a qualitative improvement in the operations management and planning models. In this work we propose a methodology to obtain operational parameter forecasts in container terminals. Moreover, a case study is developed, where forecasts of vessel performance are obtained. This way, the management strategies would be supported by an expert system, grounded on the historical data series of quay operations, and on the climatic conditions observed, as well as on the ordinary and extraordinary events that had happened in the past, from which the system is able to "learn". This work has been entirely been based on real data from a semi-automated container terminal from Spain.References
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