Multi-layer edge resource placement optimization for factories

Jakob Zietsch*, Rafal Kulaga, Harald Held, Christoph Herrmann, Sebastian Thiede

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
42 Downloads (Pure)

Abstract

Introducing distributed computing paradigms to the manufacturing domain increases the difficulty of designing and planning an appropriate IT infrastructure. This paper proposes a model and solution approach addressing the conjoint application and IT resource placement problem in a factory context. Instead of aiming to create an exact model, resource requirements and capabilities are simplified, focusing on usability in the planning and design phase for industrial use cases. Three objective functions are implemented: minimizing overall cost, environmental impact, and the number of devices. The implications of edge and fog computing are considered in a multi-layer model by introducing five resource placement levels ranging from on-device, within the production system, within the production section, within the factory (on-premise), to the cloud (off-premise). The model is implemented using the open-source modeling language Pyomo. The solver SCIP is used to solve the NP-hard integer programming problem. For the evaluation of the optimization implementation a benchmark is created using a sample set of scenarios varying the number of possible placement locations, applications, and the distribution of assigned edge recommendations. The resulting execution times demonstrate the viability of the proposed approach for small (100 applications; 100 locations) and large (1000 applications, 1000 scenarios) instances. A case study for a section of a factory producing electronic components demonstrates the practical application of the proposed approach.

Original languageEnglish
Pages (from-to)825-840
Number of pages16
JournalJournal of Intelligent Manufacturing
Volume35
Issue number2
Early online date28 Jan 2023
DOIs
Publication statusPublished - Feb 2024

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