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.

The fact that Brazil is the world's largest sugarcane (Saccharum spp.) producer, leads to a great concern about the cropping systems used, since the most common practice involves manual harvesting prior to straw burning. Brazilian authorities approved a law prohibiting the burning practice of crop residues prior to harvest. However mechanized harvest creates the new problem of how to deal with the residues. Many studies have proposed the use of these residues as an energy source. The major difficulty in its use is how to economically transport sugarcane harvested biomass from the farm to a processing center. The aim of this work was to develop a model to optimize plant variety selection, minimize the cost of the residual biomass transfer process, to evaluate the economics of using this material, and to address sucrose production and planting area constraints, considering distances from plot to processing center. For this 0-1 multiple objective linear programming techniques were used. The results show the viability of the model in selecting varieties, which provide increased profit from residual biomass use.

Se utilizó la teoría de conjuntos difusos para la modelación del problema de abonamiento y fertilización de terrenos agrícolas considerando los costos difusos. Con la ayuda de los lenguajes de programación y herramientas informáticas, se diseñó un software denominado FERTIDIF, que puede coadyuvar a la toma de una decisión apropiada sobre el uso de fertilizantes o abonos necesarios para un determinado cultivo. El problema de fertilización se resolvió mediante la adaptación de metodologías de solución, propuestos por Lai-Hwang y Leberling; metodologías que transforman un problema de programación difuso (costos difusos) en un problema de optimización multi-objetivo, el mismo que mediante la ayuda del software FERTIDIF se resuelve fácilmente y se ayuda a los especialistas en fertilización agrícola, a tomar decisiones sobre las cantidades de abonos o fertilizantes a utilizar, así como determinar el costo mínimo del proceso de fertilización de un cultivo determinado.