Robust optimization of vessel scheduling under dual uncertainties

Due to adverse weather conditions and other meteorological factors, the arrival and departure times (ADTs) of vessels, as well as sailing times in port (STPs), exhibit significant variability during actual port operations. Scheduling schemes based on deterministic ADTs and STPs often fail to meet port production demands. This paper addresses the vessel scheduling problem under uncertain arrival and departure times (UADTs) and uncertain sailing times in port (USTPs), employing a robust optimization approach. We construct two uncertainty budget sets, one to describe the ADTs and the other to describe the STPs, and establish a robust optimization model for vessel scheduling (VS) aimed to minimizing overall times occupy by vessels in port. Leveraging model features, we propose a knowledge-driven memetic algorithm (KDMA) for solving the problem. Computational experiments are conducted at the Comprehensive port in Huanghua using various instance sizes. The results demonstrate the superiority of the proposed algorithm over others.