An entropy-guided Monte Carlo tree search approach for generating optimal container loading layouts

Cant, R ORCID: https://orcid.org/0000-0001-9610-7205, Remi-Omosowon, A, Langensiepen, C ORCID: https://orcid.org/0000-0002-0165-9048 and Lotfi, A ORCID: https://orcid.org/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
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:	Number Type 10.3390/e20110866 DOI e20110866 Publisher Item Identifier
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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