scholarly journals 3D-Container Loading Problem with a Distribution Plan Based on Hybrid Quantum Genetic Algorithm

2021 ◽  
Vol 55 (4/2021) ◽  
pp. 117-132
Author(s):  
HUANG YINGYI ◽  
HUANG SHENFA ◽  
JIN CHUN
Keyword(s):  
Genetic Algorithm ◽  
Container Loading ◽  
Quantum Genetic Algorithm ◽  
Loading Problem ◽  
Container Loading Problem

A Genetic Algorithm Approach to the Automated System for Solving the Container Loading Problem

2016 ◽  
pp. 267-280
Author(s):  
Rodrigo Nogueira Cardoso ◽  
Marco Vinícius Muniz Ferreira ◽  
Alexandre Rodrigues de Sousa ◽  
José Jean-Paul Zanlucchi Souza Tavares
Keyword(s):  
Genetic Algorithm ◽  
Automated System ◽  
Container Loading ◽  
Genetic Algorithm Approach ◽  
Loading Problem ◽  
Container Loading Problem

scholarly journals Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xianbo Xiang ◽  
Caoyang Yu ◽  
He Xu ◽  
Stuart X. Zhu
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Heterogeneous Data ◽  
Adaptive Genetic Algorithm ◽  
Space Utilization ◽  
Container Loading ◽  
Related Literature ◽  
3D Space ◽  
Loading Problem ◽  
Container Loading Problem

This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.

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