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.

At automated container terminals (ACTs), twin automated stacking cranes (ASCs) can carry out the tasks—store and retrieve containers simultaneously in a yard block using a handshake bay, where a primary ASC stacks the container at the handshake bay and the other crane carries it to the destination bay. Although the handshake bay increases the degree of crane utilization, the ASCs will interfere with each other at the bay, decreasing the stacking efficiency. This study formulates a mixed-integer linear program (MILP) to position the handshake bay and simultaneously schedule the twin ASCs to minimize the tasks’ makespan. The proposed formulation considers the safe time interval to avoid crane collisions during adjacent crane movements. To solve the model, we developed a random-key genetic algorithm with a priority-based decoding scheme to optimize the task sequences and tasks assigned to the cranes. The priority-based GA can always generate feasible solutions by ranking the container-handling tasks. Numerical experiments prove that the safe temporal interval affects the makespan and the handshake bay’s position. An optimal handshake bay reduces 35% of the makespan compared with a nonoptimal bay, and the proposed algorithm is competitive compared with the on-the-shelf MILP solver and can solve medium- and large-scale instances in short computing time with gaps lower than 5% compared with ideal solutions.

Emissions of maritime transport have been a critical research topic with the substantial growth in the global shipping industry, encompassing both the expansion of the world fleet and the increased distances it has been covering recently. The International Maritime Organization (IMO) has enforced some regulations to mitigate ship Greenhouse Gas (GHG) emissions, which affect vessels’ operational practice, and further affect service reliability. In this paper, some compliance methods (two-speed strategy, fuel switching, and LNG) against Emission Control Areas (ECAs) at the operational level are examined regarding if and how they impact the liner shipping schedule and service reliability; meanwhile, uncertain weather conditions and port times, as the main uncertain factors, are also involved. Then, a bi-objective fuzzy programming model is formulated and solved by the augmented $\epsilon$-constraint approach, which generates a set of Pareto solutions by balancing the economic and environmental sustainability. Some findings can be concluded through the experimental results, including that, firstly, to meet uncertain weather conditions at sea requires strong robustness; secondly, ECA regulations can negatively affect the liner shipping service level; moreover, slow steaming is an immediate and effective measure to reduce GHG emissions; and, furthermore, ship routing choice could have a significant influence on ship emissions and service reliability.

Purpose: The paper aims to tackle the automated guided vehicles (AGVs) scheduling problem at container terminals, considering buffer location constraints and job-to-QC sequences to optimize outbound container handling, while minimizing makespan and penalty time.Design/methodology/approach: Two mixed integer programming models are developed to address the scheduling issue, incorporating practical constraints like time windows and QC blockings. The paper also presents lemmas, propositions and various algorithms (sequential insertion, random search, greedy insertion and improved genetic algorithm (GA)) for feasible scheduling.Findings: The study finds that the proposed algorithms are competitive against commercial solvers, providing effective solutions for AGV and QC scheduling, thus enhancing the efficiency of QC operations.Originality/value: The paper’s originality lies in modeling AGV queueing activities, the rescheduling process for container transport and integrated scheduling optimization of QC and AGV. It offers managerial insights through numerical experiments, showcasing the algorithms’ effectiveness.