Data-driven optimization for maritime logistics: integrating transport network mining with ship fleet routing

Abstract

We introduce a data-driven approach for optimizing maritime logistics by integrating the construction of maritime transport networks with the routing of the ship fleet. Utilizing data mining techniques, this approach identifies crucial nodes and routes from Automatic Identification System (AIS) data and builds a directed weighted transport network. Based on the obtained transport network, we then optimize ships’ routes using Mixed Integer Programming and Adaptive Large Neighborhood Search. This data-driven method provides a complete solution that improves maritime logistics from data mining to route optimization and enhances the operational autonomy of both autonomous and traditional ships. The results using real-world AIS data illustrate how data mining can be leveraged to develop a detailed transport network that significantly enhances fleet routing optimization. We evaluate our approach against two benchmarks in the literature and demonstrate that it enhances identification accuracy by over 14 %. Furthermore, through numerical analyses under various scenarios, such as route disruptions and varying levels of port congestion, our routing approach proves capable of managing large-scale operations and adapting to transport time variations. Compared to disruptions on routes, severe port congestion notably increases operational costs as it extends loading and unloading times and causes higher delay penalties.