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Scalable distributed evolutionary algorithm orchestration using Docker containers
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DOI：10.1016/j.jocs.2019.101069.png)
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Abstract
En 中文
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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Smart factory
Industry 4.0
Evolutionary algorithms
Distributed optimisation
Multi-objective optimisation
Integrated process planning and scheduling
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