|
Descripción
|
|
|---|---|
| Terminals rely on optimization tools to determine merchandise location, quay occupation or vehicle trajectories, in order to minimize the movements as well as 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, artificial intelligence systems can act as a valuable tool to analyze such processes. Neural networks in particular are characterized by their capacity to establish non-linear relationships (and consequently, non intuitive ones) among the variables; this interaction generates a specific operational response. In the near future, the monitoring of operational variables has great potential to make a qualitative improvement in the operations management and planning models of terminals that use increasing levels of automation. In this paper we propose a method to obtain operational parameter forecasts in container terminals. To this end, a case study is presented, in which forecasts of vessel performance are obtained. By doing so, the management strategies are supported by an expert system, grounded in the historical data series of quay operation and on the climatic conditions observed, as well as on the ordinary and extraordinary events that have happened in the past, from which the system is able to "learn". This research has been based entirely on data gathered from a semi-automated container terminal from Spain. | |
|
Internacional
|
Si |
|
JCR del ISI
|
Si |
|
Título de la revista
|
Journal of Waterway Port Coastal And Ocean Engineering |
|
ISSN
|
0733-950X |
|
Factor de impacto JCR
|
1,107 |
|
Información de impacto
|
Datos JCR del año 2013 |
|
Volumen
|
|
|
DOI
|
|
|
Número de revista
|
|
|
Desde la página
|
TBD |
|
Hasta la página
|
TBD |
|
Mes
|
SIN MES |
|
Ranking
|
|