Using a decision tree algorithm to predict the robustness of a transshipment schedule

Maritime ports are complex systems where any decision may affect the interests of many different stakeholders. New technologies are transforming the maritime transport sector, improving economic efficiency, optimizing the systems and operations of logistics management and enhancing connectivity. One of the problems that can benefit from the use of new technologies is finding the best transshipment schedule for incoming vessels. In this paper, we propose using automatic learning techniques to obtain a predictor of the robustness of transshipment schedules proposed by a scheduler. This measure of the goodness of the loading/unloading plan will consider the uncertainty to which processes in ports are subject, due to the weather, personnel skills, and the number of resources available, among other factors. Specifically, in this paper we propose using the so-called 'decision tree' algorithm to create a model for predicting the goodness of the proposed transshipment schedule. To obtain the historical data needed to train the machine-learning algorithm, we resorted to discrete event systems simulation, which can be used to test, for the same schedule, various scenarios with different disturbances. This yielded a model that predicts the behavior of the transshipment schedule based on the actual situation at the port. (C) 2019 The Authors. Published by Elsevier B.V.