Abstract
Effective utilization of tugboats is the key to safe and efficient transport and service in ports. With the growth of maritime traffic, more and more large seaports show a trend toward becoming super-scale, and are divided into multiple specialized terminals. This paper focuses on the problem of large-scale tugboat scheduling. An optimization problem is formulated considering the cross-region constraints and uncertainties during tugboat operation. An improved genetic algorithm is proposed based on the reversal operation (GA-RE) to solve the formulated Tug-SP. A task-triggered strategy is designed for dynamic scheduling and dealing with uncertainties. Taking Zhoushan Port as a representation of multi-terminal seaports, simulation experiments are carried out to demonstrate the effectiveness of the proposed method. Compared with historical scheduling data and the standard GA, the proposed method shows good performance in solving different scale instances (including a large-scale instance of 191 ships) in terms of solution quality and computational time.