Search

Maritime transportation plays a crucial role in the international economy. Port container terminals around the world compete to attract more traffic and are forced to offer better quality of service. This entails reducing operating costs and vessel service times. In doing so, one of the most important problems they face is the Berth Allocation and quay Crane Assignment Problem (BACAP). This problem consists of assigning a number of cranes and a berthing time and position to each calling vessel, aiming to minimize the total cost. An extension of this problem, known as the BACAP Specific (BACASP), also involves determining which specific cranes are to serve each vessel. In this paper, we address the variant of both BACAP and BACASP consisting of a continuous quay, with dynamic arrivals and time-invariant crane-to-vessel assignments. We propose a metaheuristic approach based on a Biased Random-key Genetic Algorithm with memetic characteristics and several Local Search procedures. The performance of this method, in terms of both time and quality of the solutions obtained, was tested in several computational experiments. The results show that our approach is able to find optimal solutions for some instances of up to 40 vessels and good solutions for instances of up to 100 vessels. [ABSTRACT FROM AUTHOR]

This paper extends the study of the continuous Berth Allocation Problem to the case of multiple quays, which is found in many container terminals around the world. Considering multiple quays adds a problem of assigning vessels to quays to the problem of determining berthing times and positions for each incoming vessel. This problem has not been considered in the literature at the operational level addressed in this study. In order to solve it, we have developed first an integer linear model and then a genetic algorithm which works on sequences of vessels that are decoded by a constructive algorithm. A local search procedure helps to improve further the solutions produced by the genetic algorithm. We have conducted an extensive computational experiment, adapting existing instances to the case of multiple quays. We have also adapted our genetic algorithm to the case of a single quay and tested it on previously reported instances, showing its superior performance compared with existing approaches. Finally, we have developed a random instance generator for the problem with multiple quays, enabling us to conduct a systematic study of the factors affecting the complexity of the problem. [ABSTRACT FROM AUTHOR]