To solve the problem of storage space allocation for both inbound and outbound containers and twin automated stacking cranes scheduling in the automated container terminals, a new repositioning strategy is designed and a cooperative optimization model is established. The model considers the constraints of safety distance and handshake area capacity. The objective of the model is to minimize the makespan of all storage requests and the total rehandling time during the retrieval process, which is calculated according to the priority of inbound and outbound containers defined by the early retrieval and early loading rule respectively. A variable neighborhood search based hybrid genetic algorithm is designed to solve the model. Numerical experiments show that the sum of the makespan of all storage requests and the total rehandling time during the retrieval process is reduced by approximately 10% compared to the traditional repositioning strategy by optimizing the sequence of container repositioning from the handshake area to the seaside or landside designated storage space. The research is beneficial to improve the operation efficiency of automated container terminal yards.

This study proposes a formulation to optimize operational efficiency of a dual-trolley quay crane and automatic guided vehicles (AGVs) to reduce energy consumption at an automated container terminal. A two-phase model is used to minimize energy consumption during loading and discharging operations, as well as maximize the utilization rate of the AGVs, with consideration of relevant constraints such as the capacity of buffers for the quay crane (QC) and yard, the stability of vessel, the maximum endurance of an AGV, and the available laytime for handling. We propose a constrained partial enumeration strategy to construct quay crane schedules and a genetic algorithm to solve the AGV scheduling problem. Finally, Yangshan Phase IV automated container terminal’s data is used to verify the validity and applicability of the proposed model. The results of the tests provide evidence that the proposed method can improve energy efficiency.

The International Maritime Organization (IMO) proposed to reduce the total CO₂ emissions of the maritime sector by 50% by 2050, and strive to gradually achieve the zero-carbon target. Therefore, shipping companies need to consider environmental impacts while pursuing benefits. In view of the tramp ship scheduling with speed optimization problem, considering carbon emissions, the configuration of owner ships and charter ships, and the impact of sailing speed on ship scheduling with the target of minimizing the total costs of shipping companies, multi-type tramp ship scheduling and speed optimization considering carbon emissions is established. A genetic simulated annealing algorithm based on a variable neighborhood search is proposed to solve the problem. Firstly, the ship type is matched with the cargo. Then the route is generated according to the time constraint, and finally, the neighborhood search strategy is adopted to improve the solution quality. The effectiveness of the proposed model and algorithm is verified by an example, which also confirms that ship scheduling and sailing speed joint optimization can reduce costs and carbon emissions. Research results can not only deepen the study of the theory of tramp scheduling but also to effectively solve the tramp shipping schedule considering carbon emissions problems faced by companies to provide theoretical guidance.

Cruise industry presents an active and rapid development in recent years. Due to the rich tourism resource and potential tourism market, China has become the core component of the Asian market. Identifying factors that affect cruise homeport selection systematically and designing scientific evaluation system are the keys to selecting the optimal cruise homeport location. We analysed the influence factors motivating the cruise liner companies to select a specific port as the cruise homeport and designed an index system includes both quantitative indices and qualitative indices following the basic principles of index selection. In order to resolve the problem of incomplete information and randomness of the decision-making process, the grey-cloud clustering model was used for evaluation of cruise homeport selection. Further, we conducted a case study to evaluate whether or not these nine cruise ports, such as Shanghai, Tianjin, Shenzhen and Sanya, are eligible to be homeports in China. Finally, several suggestions to improve the chance of being selected as a cruise homeport were recommended from viewpoints of port facilities, services and policy conditions. The results show that the model can reasonably and effectively help decision-makers to facilitate the homeport selection in real operations.

Irregular external truck arrivals at a marine container terminal often leads to long queues at gates and substantial greenhouse gas emissions. To relieve gate congestion and reduce carbon emissions, a new truck arrival pattern called “vessel dependent time windows (VDTWs)” is proposed. A two-phase queuing model is established to describe the queuing process of trucks at gate and yard. An optimization model is established to assign time window and appointment quota for each vessel in a marine container terminal running a terminal appointment system (TAS) with VDTWs. The objective is to minimize the total carbon dioxide emissions of trucks and rubber-tired gantry cranes (RTGCs) during idling. The storage capacity constraints of each block and maximum queue length are also taken into consideration. A hybrid genetic algorithm based on simulated annealing is developed to solve the problem. Results based on numerical experiments demonstrate that this model can substantially reduce the waiting time of trucks at gate and yard and carbon dioxide emissions of trucks and RTGCs during idling.

Aiming at the truck scheduling problem between the outer yard and multi-terminals, the appointment optimization model of truck is established. In this model, the queue time and the operation time of truck during the appointment period of different terminals are different. Under the restriction of given appointment quotas of each appointment period, determine the arrival amount of trucks in each appointment period. The goal is to reduce carbon emissions and total costs, improve the efficiency of truck scheduling. To solve this model, hybrid genetic algorithm with variable neighborhood search was designed. Firstly, generate chromosomes, and the front part of the chromosome represents the demand for 40 ft containers and the back part represents the demand for 20 ft containers. Then, the route is generated according to the time constraint and appointment quotas of each appointment period. Finally, the neighborhood search strategy is adopted to improve the solution quality. The validity of the model and algorithm were verified by an example. A low-carbon scheduling scheme was obtained under truck appointment system. The results show that the scheduling scheme under truck appointment system uses fewer trucks, improves the efficiency of delivery, reduces the total costs, and it takes into account the requirements of low carbon.