A Study on Container Storage Optimization in Yards Based on a Hyper-Heuristic Algorithm with a Q-Learning Mechanism

In the context of a low-carbon economy, scientific methods to reduce carbon emissions have become an important issue for many ports. Carbon emissions in port areas mainly arise from vessels and handling equipment. Therefore, an effective resource assignment and equipment arrangement system could not...

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Bibliographic Details
Published in:International journal of computational intelligence systems Vol. 18; no. 1; pp. 163 - 24
Main Authors: Chen, Lifen, Lin, Jiajun, Xu, Shihao
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 30.06.2025
Springer Nature B.V
Springer
Subjects:
Algorithms
Artificial Intelligence
Carbon
Carbon emissions
Computational Intelligence
Container rehandling operation
Containers
Control
Efficiency
Emissions
Energy consumption
Engineering
Genetic algorithms
Handling equipment
Heuristic
Heuristic algorithm
Heuristic methods
Hyper-heuristic algorithm
Integer programming
Linear programming
Literature reviews
Machine learning
Maritime industry
Mathematical Logic and Foundations
Mechatronics
Mixed integer
Optimization
Optimization algorithms
Ports
Research Article
Robotics
Simulation
Hyper-heuristic algorithm
Heuristic algorithm
Carbon emissions
Container rehandling operation
ISSN:1875-6883, 1875-6891, 1875-6883
Online Access:Get full text
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Summary:In the context of a low-carbon economy, scientific methods to reduce carbon emissions have become an important issue for many ports. Carbon emissions in port areas mainly arise from vessels and handling equipment. Therefore, an effective resource assignment and equipment arrangement system could not only reduce carbon emissions, but also improve the port’s operational efficiency. This study considers factors such as the arrival order of container trailers, the cargo weight, and the number of container rehandling operations. The objective is to minimize the carbon emissions and the number of container rehandling operations in ports, for which a mixed-integer linear programming model is built. Both heuristic algorithms and hyper-heuristic algorithms are employed to optimize the container storage plan, and their applicability in storage optimization is compared. The results indicate that hyper-heuristic algorithms outperform heuristic algorithms in terms of solution quality and stability, effectively satisfying the storage requirements of the yard while minimizing the carbon emissions and the number of container rehandling operations. The results provide theoretical support for port enterprises in improving their operational efficiency and achieving their goals regarding low carbon emissions.
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ISSN:1875-6883
1875-6891
1875-6883
DOI:10.1007/s44196-025-00905-5