Early Detection Vessel Delays using Combined Historical and Real-time Information

Abstract

Detecting vessel delays in advance or in real time is important in order to fulfill the expectations of customers and to help customers reduce delay costs. However, the early detection of vessel delays faces the challenges of numerous uncertainties, including weather conditions, port congestion, booking issues, and route selection. This paper proposes a data-driven method for the early detection of vessel delays: in our new framework of refined case-based reasoning, real-time S-AIS vessel tracking data are utilized in combination with historical shipping data. Real data examples from a logistics company demonstrate the effectiveness of the proposed method.