This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times. The model optimizes the use of the quays. The BAP + QCAP, is a NP-hard (Non-deterministic polynomial-time hardness) combinatorial optimization problem, where the decision to assign available quays for each vessel adds more complexity. The imprecise vessel arrival times and the decision variables—berth and departure times—are represented by triangular fuzzy numbers. The model obtains a robust berthing plan that supports early and late arrivals and also assigns cranes to each berth vessel. The model was implemented in the CPLEX solver (IBM ILOG CPLEX Optimization Studio); obtaining in a short time an optimal solution for very small instances. For medium instances, an undefined behavior was found, where a solution (optimal or not) may be found. For large instances, no solutions were found during the assigned processing time (60 min). Although the model was applied for n = 2 quays, it can be adapted to “n” quays. For medium and large instances, the model must be solved with metaheuristics.

The berth allocation problem (BAP) in marine container terminals is defined as the feasible berth allocation to the incoming vessels. In this work, we develop a model of fully fuzzy linear programming (FFLP) for the continuous and dynamic BAP. The vessel arrival times are assumed to be imprecise, meaning that the vessel can be late or early up to a threshold permitted. Triangular fuzzy numbers represent the uncertainty of the arrivals. The model proposed has been implemented in CPLEX and evaluated for different instances. The results obtained show that the model proposed is helpful to the administrators of a marine container terminal, since a plan supporting imprecision in the arrival time of vessels, optimized with respect to the waiting time and easily adaptable to possible incidents and delays, is available to them.

Se ha aplicado redes neuronales artificiales a los datos climáticos de precipitación, temperaturas superficiales de mar en diferentes zonas calificadas como Niño y la velocidad de los vientos alisios con el fin de modelar y predecir el fenómeno El Niño, con seis meses de anticipación a la presencia del mismo. El estudio se realiza en Piura, Perú. Se realiza un análisis preliminar de la información para determinar el grado de correlación entre las variables, luego se ha diseñado el modelado en dos fases, la primera usa las redes neuronales para modelar las variables como series temporales y en la segunda fase se diseña una red neuronal usando MatLab para que simule el comportamiento de las precipitaciones en Piura. Los resultados del estudio muestran que las redes neuronales son una técnica altamente confiable para encontrar un patrón de comportamiento de las precipitaciones y luego para predecir el fenómeno alcanzando una tasa de acierto del 98.4% en la etapa de entrenamiento y de 100% en la predicción del primer semestre del año 2016.