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With the normalization of the global epidemic, shipping has gradually resumed, resulting in a surge in port throughput. Terminal managers are having great difficulty in making container ports be stable, orderly, and efficient. For small and medium-sized ports, making full use of the tidal water level to increase the operation time of large ships significantly improves the efficiency of port operations. Therefore, considering the impact of tidal factors on the operation of container ports, this paper firstly proposes a new berths and quay cranes allocation optimization model, T-B&QC, that minimizes the distance between the actual berthing berth and the preferred berth, the port time cost of ships and the number of quay crane movements as the optimization objectives, under constraints that consider tidal factors. Then, to solve the T-B&QC model using chaotic mapping and quantum theory, the Whale Optimization Algorithm (WOA) is integrated by using the Chaotic Quantum Rotating Gate Algorithm (CQRGA) and the Quantum Not Gate Algorithm (QNGA), whale coding rules are designed, and the Feasible-Integer Processing Algorithm (WF-IP) is established. Afterwards, the Chaotic Quantum Whale Optimization Algorithm, CQWOA, is proposed. Finally, the CQWOA is used to develop a new berth and quay crane allocation optimization method, T-B&QC_CQWOA. Subsequently, data from an actual seaport container port is used to test the reliability and superiority of the proposed distributing approach and the established optimizing algorithm. Numerical results demonstrate that the proposed distributing approach outperforms the classical distribution models that are selected herein, and the CQWOA yields a coordinated schedule of higher quality than the others in the process of solving the model. • A new berths and quay cranes allocation optimization model is modeled to satisfy the distribution plan. • This paper applies chaos mapping and quantum theory to improve the nonlinear convergence factor. • This paper designs the solution process of the whale feasible integer algorithm based on the chaotic and quantum theory. [ABSTRACT FROM AUTHOR]

Automated container terminals (ACTs) represent state-of-the-art facilities for container handling and are a current development trend. However, enhancing their operational efficiency while minimizing energy consumption remains a challenge. While metaheuristics are helpful in addressing container terminal problems, their capabilities are limited when used alone to tackle complex or integrated problems. Hybrid models can be more effective in facing these challenges. This research proposes a hybrid model, termed WOA + PSO, which combines the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) as a novel approach to address the integrated scheduling problem of automated quay cranes (AQCs), automated lift vehicles (ALVs), and automated stacking cranes (ASCs) in an ACT. Additionally, the WOA + PSO collaborates with a simulation model in a framework to become a simulation-based optimization approach. The performance of WOA + PSO is evaluated by comparing it with its base models, WOA and PSO, as well as a genetic algorithm (GA), through extensive experiments. The results show that WOA + PSO outperforms the others in achieving the objective of balancing operational efficiency and energy consumption. [ABSTRACT FROM AUTHOR]

With the development of information technology and automation, intelligence techniques have gradually replaced manpower in container terminals. Automating container terminals can significantly improve the operation efficiency of the terminals and reduce energy consumption, time, and transportation resources. Automated guided vehicles (AGVs), used to transport containers between the seaside and the yard side, are very important for automated container terminal (ACT) performance. Meanwhile, container terminals lack systematic resilience and often operate poorly after emergencies. This study considers the problem of resilient scheduling AGVs with battery constraints. We consider the different power consumption of AGVs under loaded and empty conditions and the nonlinearity of battery charging capacity and charging time. In this model, an improved charging strategy, consisting of two thresholds for disconnecting the charging power supply, is proposed for a resilient scheduling model to minimize the total operation time to complete the transport tasks. Furthermore, a new metaheuristic algorithm that uses an adaptive large neighborhood search was proposed to solve the problem. Finally, we tested our improved charging strategy and proposed algorithm in a real case of a large Chinese ACT in the Peral River Delta. Computational results indicate that the proposed AGV charging strategy exhibits effective and efficient performance for ACTs. The sensitivity analysis also shows how the number of AGVs affects the total transport task completion time and the AVG utilization rate. [ABSTRACT FROM AUTHOR]

The container relocation problem (CRP) is an NP-hard combinatorial optimisation problem that arises in yard management. The problem is concerned with loading all containers from the storage yard to the ship in a certain order. The yard layout consists of bays where containers are placed in stacks on top of each other, and each container has a due date that determines their retrieval order. Due to its complexity, heuristic methods are used to solve CRP, ranging from relocation rules to metaheuristics. Relocation rules (RRs) are used when the goal is to obtain a solution of acceptable quality in short time. Manually designing RRs is difficult and time-consuming, which motivates the use of different methods to automatically design RRs. In this study, we investigate the application of genetic programming (GP) to design RRs for CRP with multiple bays and container groups. The GP algorithm was adapted for generating RRs by proposing a new set of terminals and several solution construction methods. The proposed method was evaluated on an extensive benchmark of existing problems. The results obtained with automatically developed RRs were compared with the results of manually designed RRs and it was found that the automatically designed RRs performed significantly better in all cases. • Non standard problems are more difficult to solve with manual relocation rules. • Genetic programming can automatically generate new relocation rules. • Better results obtained as the number of duplicate containers increases. • Crane operation time is a pivotal criterion in multibay problems. • Automatically designed rules outperform manual rules by up to 7%. [ABSTRACT FROM AUTHOR]