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
Item Type: | Article |
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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: | Funder Grant number EUROPEAN COMMISSION 723634 |
Depositing User: | Pure (York) |
Date Deposited: | 02 Jan 2020 12:00 |
Last Modified: | 06 Feb 2025 00:09 |
Published Version: | https://doi.org/10.1016/j.jocs.2019.101069 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.jocs.2019.101069 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155045 |