Advancing multi-port container stowage efficiency: A novel DQN-LNS algorithmic solution

Ling, Yu; Wang, Qianlong; Pan, Lin

doi:10.1016/j.knosys.2024.112074

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2024.112074

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Advancing multi-port container stowage efficiency: A novel DQN-LNS algorithmic solution

Yu Ling,

Qianlong Wang,

Lin Pan

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Cited by 2 publications

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Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

Cho,

2024

JMSE

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This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics.

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Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

Cho,

2024

JMSE

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Collaborative Optimization of Vessel Stowage Planning and Yard Pickup in Automated Container Terminals

Huang,

Wang,

Zhang

2024

Mathematics

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The aim of this research is to minimize the number of container-rehandling operations in both the yard and on the ship in order to solve the problem of coordinated optimization of ship loading and yard container retrieval and enhance the loading efficiency of automated container terminals,. An optimization model that integrates the optimized decision-making of both the container retrieval order from the yard and the ship’s space allocation is developed, and an improved cuckoo algorithm is employed to solve the optimization problem. This paper examines the influence of container retrieval order from the yard on rehandling within the yard, as well as the effect of the ship’s space allocation on subsequent rehandling operations at the unloading port. Experimental results demonstrate that the solution derived from the comprehensive optimization model based on the improved cuckoo algorithm considerably reduces the overall number of overturned containers. This confirms that the proposed optimization model effectively enhances loading efficiency and reduces container rehandling both in the yard and on the ship. The analysis of experimental results indicates that the model and algorithm proposed in this paper have certain applicability.

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Advancing multi-port container stowage efficiency: A novel DQN-LNS algorithmic solution

Cited by 2 publications

References 32 publications

Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

Collaborative Optimization of Vessel Stowage Planning and Yard Pickup in Automated Container Terminals

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