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This work has been funded by European Commission trough the Horizon Europe project "Multi -Modal and Multi -Aspect Holistic Human-Robot Interaction (FORTIS) ", grant ID 101135707. This work was also partially funded by Ministerio de Ciencia, Innovacion y Universidades (MICIU) of Spain trough the project "Self -reconfiguration for Industrial Cyber -Physical Systems based on digital twins and Artificial Intelligence. Methods and application in Industry 4.0 pilot line (SELFRECO) ", grant ID PID2021-127763OB-100". This work was also supported in part by the project "Digitalization of Power Electronic Applications within Key Technology Value Chains (PowerizeD) ", grant ID 101096387, funded by the European Union HORIZON Framework Programme and Chips JU.

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Automated machine learning methodology for optimizing production processes in small and medium-sized enterprises

Publicat a:Operations Research Perspectives. 12 100308- - 2024-06-01 12(), DOI: 10.1016/j.orp.2024.100308

Autors: Cruz, YJ; Villalonga, A; Castaña, F; Rivas, M; Haber, RE

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Machine learning can be effectively used to generate models capable of representing the dynamic of production processes of small and medium-sized enterprises. These models enable the estimation of key performance indicators, and are often used for optimizing production processes. However, in most industrial applications, modeling and optimization of production processes are currently carried out as separate tasks, manually in a very costly and inefficient way. Automated machine learning tools and frameworks facilitate the path for deriving models, reducing modeling time and cost. However, optimization by exploiting production models is still in infancy. This work presents a methodology for integrating a fully automated procedure that embraces automated machine learning pipelines and a multi-objective optimization algorithm for improving the production processes, with special focus on small and medium-sized enterprises. This procedure is supported on embedding the generated models as objective functions of a reference point based non-dominated sorting genetic algorithm, resulting in preference-based Pareto-optimal parametrizations of the corresponding production processes. The methodology was implemented and validated using data from a manufacturing production process of a small manufacturing enterprise, generating highly accurate machine learning-based models for the analyzed indicators. Additionally, by applying the optimization step of the proposed methodology it was possible to increase the productivity of the manufacturing process by 3.19 % and reduce its defect rate by 2.15 %, outperforming the results obtained with traditional trial and error method focused on productivity alone.

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