A Machine Learning-based system for berth scheduling at bulk terminals

The increasing volume of maritime freight is presented as a challenge to those skilled terminal managers seeking to maintain or increase their market share. In this context, an efficient management of scarce resources as berths arises as a reasonable option for reducing costs while enhancing the productivity of the overall terminal. In this work, we tackle the berth scheduling operations by considering the Bulk Berth Allocation Problem (Bulk-BAP). This problem, for a given yard layout and location of the cargo facilities, aims to coordinate the berthing and yard activities for giving service to those vessels arriving at the terminal. Considering the multitude of scenarios arising in this environment and theNo Free Lunch theorem, the drawback concerning the selection of the best algorithm for solving the Bulk-BAP in each particular case is addressed by a Machine Learning-based system. It provides, based on the scenario at hand, a ranking of algorithms sorted by appropriateness. The computational study shows an increase in the quality of the provided solutions when the algorithm to be used is selected according to the features of the instance instead of selecting the best algorithm on average.