Abstract
Manufacturing scheduling has a crucial role in a company's performance. It’s a hard optimization problem and due to the latest manufacturing trends, it is becoming even more complex. Metaheuristics are promising methods to solve those real-world problems. The latest distributed/parallel computing advances may support the increase of computational power needed to get efficient schedules a suitable time period. In the last years, the Industrial Internet has also known some advances as the emergence of the Edge computing paradigm that increased the computational processing power near the factory floor. This work presents strategies to implement a distributed metaheuristic for manufacturing scheduling on the Edge. Under the scheduling problem context, the physical platform and the programming environment are examined. Based on an evolutionary metaheuristic (genetic algorithm), a model is developed, following strategies that take advantage of the Edge layer of the Industrial Internet. The generic algorithm steps are described for future deployment and validation.
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Acknowledgements
This research is sponsored by FEDER funds through the program COMPETE – Programa Operacional Factores de Competitividade – and by national funds through FCT – Fundação para a Ciência e a Tecnologia –, under the project UIDB/00285/2020 and the doctoral grant to Pedro Coelho (SFRH/BD/129714/2017).
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Coelho, P., Silva, C. (2021). A Distributed Model for Manufacturing Scheduling: Approaching the EDGE. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_44
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