The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans ce travail, une méthode d'optimisation pour le problème de chargement de conteneurs 3D avec contraintes multiples est proposée. La méthode consiste en un algorithme génétique pour générer un agencement de marchandises et une évaluation de la condition physique à l'aide d'une simulation physique. La fonction d'aptitude prend en compte non seulement la maximisation de la densité et de la valeur d'aptitude du conteneur, mais également plusieurs contraintes différentes telles que le poids, la capacité d'empilage, la fragilité et l'orientation des pièces de cargaison. Nous avons utilisé une simulation de secousses de conteneurs pour l'évaluation de la condition physique afin d'inclure les effets de contrainte pendant le chargement et le transport. Nous avons vérifié que la méthode proposée fournit avec succès la disposition optimale des marchandises pour des problèmes à petite échelle impliquant environ 10 pièces de marchandises.
Shuhei NISHIYAMA
Osaka University
Chonho LEE
Osaka University,Okayama University of Science
Tomohiro MASHITA
Osaka University
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Shuhei NISHIYAMA, Chonho LEE, Tomohiro MASHITA, "Solving 3D Container Loading Problems Using Physics Simulation for Genetic Algorithm Evaluation" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 11, pp. 1913-1922, November 2021, doi: 10.1587/transinf.2020EDP7239.
Abstract: In this work, an optimization method for the 3D container loading problem with multiple constraints is proposed. The method consists of a genetic algorithm to generate an arrangement of cargo and a fitness evaluation using a physics simulation. The fitness function considers not only the maximization of the container density and fitness value but also several different constraints such as weight, stack-ability, fragility, and orientation of cargo pieces. We employed a container shaking simulation for the fitness evaluation to include constraint effects during loading and transportation. We verified that the proposed method successfully provides the optimal cargo arrangement for small-scale problems with about 10 pieces of cargo.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7239/_p
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@ARTICLE{e104-d_11_1913,
author={Shuhei NISHIYAMA, Chonho LEE, Tomohiro MASHITA, },
journal={IEICE TRANSACTIONS on Information},
title={Solving 3D Container Loading Problems Using Physics Simulation for Genetic Algorithm Evaluation},
year={2021},
volume={E104-D},
number={11},
pages={1913-1922},
abstract={In this work, an optimization method for the 3D container loading problem with multiple constraints is proposed. The method consists of a genetic algorithm to generate an arrangement of cargo and a fitness evaluation using a physics simulation. The fitness function considers not only the maximization of the container density and fitness value but also several different constraints such as weight, stack-ability, fragility, and orientation of cargo pieces. We employed a container shaking simulation for the fitness evaluation to include constraint effects during loading and transportation. We verified that the proposed method successfully provides the optimal cargo arrangement for small-scale problems with about 10 pieces of cargo.},
keywords={},
doi={10.1587/transinf.2020EDP7239},
ISSN={1745-1361},
month={November},}
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TY - JOUR
TI - Solving 3D Container Loading Problems Using Physics Simulation for Genetic Algorithm Evaluation
T2 - IEICE TRANSACTIONS on Information
SP - 1913
EP - 1922
AU - Shuhei NISHIYAMA
AU - Chonho LEE
AU - Tomohiro MASHITA
PY - 2021
DO - 10.1587/transinf.2020EDP7239
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2021
AB - In this work, an optimization method for the 3D container loading problem with multiple constraints is proposed. The method consists of a genetic algorithm to generate an arrangement of cargo and a fitness evaluation using a physics simulation. The fitness function considers not only the maximization of the container density and fitness value but also several different constraints such as weight, stack-ability, fragility, and orientation of cargo pieces. We employed a container shaking simulation for the fitness evaluation to include constraint effects during loading and transportation. We verified that the proposed method successfully provides the optimal cargo arrangement for small-scale problems with about 10 pieces of cargo.
ER -