By the same authors

Scalable Distributed Evolutionary Algorithm Orchestration using Docker Containers

Research output: Contribution to journalArticlepeer-review

Full text download(s)

Published copy (DOI)



Publication details

JournalJournal of Computational Science
DateAccepted/In press - 20 Dec 2019
DateE-pub ahead of print (current) - 7 Jan 2020
Number of pages14
Pages (from-to)1-14
Early online date7/01/20
Original languageEnglish


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.

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.

    Research areas

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



    Project: Research project (funded)Research

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations