Dziurzanski, Piotr, Zhao, Shuai, Przewozniczek, Michael et al. (2 more authors) (2020) Scalable Distributed Evolutionary Algorithm Orchestration using Docker Containers. Journal of Computational Science. 101069. pp. 1-14. ISSN 1877-7503
Abstract
In smart factories, integrated optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation for process planning and scheduling is presented. Three managers dynamically governing the creation and deletion of subpopulations (islands) evolved by a multi-objective genetic algorithm are proposed, compared and contrasted. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. | ||||
Keywords: | Smart factory, Industry 4.0, Evolutionary algorithms, Distributed optimisation, Multi-objective optimisation, Integrated process planning and scheduling | ||||
Dates: |
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Institution: | The University of York | ||||
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) | ||||
Funding Information: |
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Depositing User: | Pure (York) | ||||
Date Deposited: | 02 Jan 2020 12:00 | ||||
Last Modified: | 04 Feb 2024 01:04 | ||||
Published Version: | https://doi.org/10.1016/j.jocs.2019.101069 | ||||
Status: | Published online | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.jocs.2019.101069 |