Predictive Analysis for Optimizing Port Operations
Aniruddha Rajendra Rao Haiyan Wang Chetan Gupta
Maritime transport is a pivotal logistics mode for the long-distance and bulk
transportation of goods. However, the intricate planning involved in this mode
is often hindered by uncertainties, including weather conditions, cargo
diversity, and port dynamics, leading to increased costs. Consequently,
accurately estimating vessel total (stay) time at port and potential delays
becomes imperative for effective planning and scheduling in port operations.
This study aims to develop a port operation solution with competitive
prediction and classification capabilities for estimating vessel Total and
Delay times. This research addresses a significant gap in port analysis models
for vessel Stay and Delay times, offering a valuable contribution to the field
of maritime logistics. The proposed solution is designed to assist
decision-making in port environments and predict service delays. This is
demonstrated through a case study on Brazil ports. Additionally, feature
analysis is used to understand the key factors impacting maritime logistics,
enhancing the overall understanding of the complexities involved in port
operations.