Scalable Distributed Evolutionary Algorithm Orchestration using Docker Containers

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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.
Original languageEnglish
Article number101069
Pages (from-to)1-14
Number of pages14
JournalJournal of Computational Science
Early online date7 Jan 2020
Publication statusE-pub ahead of print - 7 Jan 2020

Bibliographical note

© 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.


  • Smart factory
  • Industry 4.0
  • Evolutionary algorithms
  • Distributed optimisation
  • Multi-objective optimisation
  • Integrated process planning and scheduling

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