IEICE TRANSACTIONS on Information
Solving 3D Container Loading Problems Using Physics Simulation for Genetic Algorithm Evaluation
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.11 pp.1913-1922
- Publication Date
- 2021/11/01
- Publicized
- 2021/08/06
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7239
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Shuhei NISHIYAMA
Osaka University
Chonho LEE
Osaka University,Okayama University of Science
Tomohiro MASHITA
Osaka University
Keyword
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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},}
Copy
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 -
English
