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.

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.

Due to mega-ships, increasing container throughput, and nonuniform truck arrivals, many container terminals face challenges of unbalanced workloads of yard equipment, shortage of equipment resources in peak hours, and congestion problem. To solve such issues, we propose a mixed-integer bilevel programming model to optimize the vessel-dependent time windows for inbound trucks and yard crane deployment simultaneously. In the proposed bilevel model, the upper level aims to minimize the total truck waiting time at the container terminal gate and yard, while the lower level is formulated to minimize the total workload overflow to next shift in the whole container yard. The optimal yard crane deployment obtained in the lower level will transfer to the upper level problem to determine the waiting time of trucks in the yard and then affect the truck arrivals pattern. To solve the model, a hybrid algorithm—called hybrid genetic algorithm, based on collective decision optimization—is put forward by combining the genetic algorithm and the collective decision optimization algorithm. Numerical experiments are conducted to validate the proposed approach is effective to simultaneously flatten truck arrivals and improve the efficiency of yard cranes. The proposed approach can significantly reduce container terminals’ truck waiting time.