Machine Learning-Based Approach to Liner Shipping Schedule Design

This paper studied a tactical liner shipping schedule design issue under sail and port time uncertainties, which is the determination of the planned arrival time at each port call as well as the punctuality rate and number of assigned ship on the route. A number of studies have tried to introduce the operational speed adjustment measure into this tactical schedule design issue, to alleviate the discrepancies between designed schedule and maritime practice. On the one hand, weather conditions can lead to speed loss phenomenon of ships, which may result in the failure of ships’ punctual arrivals. On the other hand, improving the ability of speed adjustment can decrease the late-arrival compensation, but increase the fuel consumption cost. Then, we formulated a machine learning-based liner shipping schedule design model aiming at above-mentioned two limitations on speed adjustment measure. And a machine learning-based approach has been designed, where the speed adjustment simulation, the neural network training and the reinforcement learning were included. Numerical experiments were conducted to validate our results and derive managerial insights, and then the applicability of machine learning method in shipping optimization issue has been confirmed. © 2021, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.