{rfName}
Au

Indexed in

License and use

Icono OpenAccess

Citations

5

Altmetrics

Article has an altmetric score of 14

Grant support

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.

Analysis of institutional authors

Cruz, Yarens JCorresponding AuthorVillalonga A.AuthorVillalonga, AlbertoAuthorCastaño F.AuthorHaber, Rodolfo EAuthor

Share

Publications
>
Article

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

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

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

Affiliations

Univ Matanzas, Ctr Estudios Fabricac Avanzada & Sostenible, Matanzas 40100, Cuba - Author
Univ Politecn Madrid, Ctr Automat & Robot, CSIC, Madrid 28500, Spain - Author

Abstract

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.

Keywords

Automated machine learningAutomlFeature-selectionHyperparameter optimizationModel selectionMulti -objective optimizatioMulti-objective optimizationOptimizationPredictioR-nsga-iiRandom search algorithm

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Operations Research Perspectives due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Control and Optimization.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-06-14:

  • WoS: 1
  • Scopus: 4

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-14:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 110.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 110 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 13.75.
  • The number of mentions on the social network X (formerly Twitter): 5 (Altmetric).
  • The number of mentions in news outlets: 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Cuba.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (CRUZ HERNÁNDEZ, YARENS JOAQUÍN) and Last Author (HABER GUERRA, RODOLFO ELIAS).

the author responsible for correspondence tasks has been CRUZ HERNÁNDEZ, YARENS JOAQUÍN.