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Cant, R. 
ORCID: 0000-0001-9610-7205, Remi-Omosowon, A., Langensiepen, C. ORCID: 0000-0002-0165-9048 and Lotfi, A. ORCID: 0000-0002-5139-6565,
2018.
An entropy-guided Monte Carlo tree search approach for generating optimal container loading layouts.
Entropy, 20 (11): 866.
ISSN 1099-4300
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Abstract
In this paper, a novel approach to the container loading problem using a spatial entropy measure to bias a Monte Carlo Tree Search is proposed. The proposed algorithm generates layouts that achieve the goals of both fitting a constrained space and also having “consistency” or neatness that enables forklift truck drivers to apply them easily to real shipping containers loaded from one end. Three algorithms are analysed. The first is a basic Monte Carlo Tree Search, driven only by the principle of minimising the length of container that is occupied. The second is an algorithm that uses the proposed entropy measure to drive an otherwise random process. The third algorithm combines these two principles and produces superior results to either. These algorithms are then compared to a classical deterministic algorithm. It is shown that where the classical algorithm fails, the entropy-driven algorithms are still capable of providing good results in a short computational time.
| Item Type: | Journal article | ||||||
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| Publication Title: | Entropy | ||||||
| Creators: | Cant, R., Remi-Omosowon, A., Langensiepen, C. and Lotfi, A. | ||||||
| Publisher: | MDPI | ||||||
| Date: | 9 November 2018 | ||||||
| Volume: | 20 | ||||||
| Number: | 11 | ||||||
| ISSN: | 1099-4300 | ||||||
| Identifiers: |
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| Rights: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Jill Tomkinson | ||||||
| Date Added: | 19 Nov 2018 16:07 | ||||||
| Last Modified: | 19 Nov 2018 16:07 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/35061 |
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