Multi-Objective Optimization for Ship Scheduling with Port Congestion and Environmental Considerations

Abstract

Over the past several years, port congestion has become a severe problem, as ships are often not able to reach a series of ports based on the designed schedule, which induces changes in the schedules associated with port operations. Moreover, customers can not receive their cargo in a timely manner because of port congestion. This is not only an internal problem within the shipping industry but also calls for collaboration between shipping lines and their upstream or downstream members in the maritime supply chain, including shippers and port operators. This study concentrates on the tactical planning problem for optimizing ship schedules to determine the number of ships, the projected maximum speed, and the ship service schedule, which is set for a company on a certain route. We develop a novel multi-objective programming model for the green vessel scheduling problem under port congestion, and queuing theory is used to calculate the uncertain queuing times at ports. The ultimate goal of developing this model is to maximize cost efficiency, service reliability, and environmental benefits. A multi-objective grey wolf optimizer algorithm is introduced for solving this problem, which shows some computational advantages compared to the NSGA-II algorithm commonly used at the most advanced level. Experimental results verify the application of the model and confirm that more congested periods induce more service unreliability issues rather than additional costs and emissions generated. To this end, the proposed methodology would allow designing better liner shipping schedules to alleviate port congestion and provide sustainable shipping services.