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An intelligent decision support system for production planning based on machine learning

  • Germán González RodríguezEmail author
  • Jose M. Gonzalez-Cava
  • Juan Albino Méndez Pérez
Article
  • 83 Downloads

Abstract

This paper presents a new methodology to solve a Closed-Loop Supply Chain (CLSC) management problem through a decision-making system based on fuzzy logic built on machine learning. The system will provide decisions to operate a production plant integrated in a CLSC to meet the production goals with the presence of uncertainties. One of the main contributions of the proposal is the ability to reject the effects that the imbalances in the rest of the chain have on the inventories of raw materials and finished products. For this, an intelligent algorithm will be in charge of the supervision of the plant operation and task-reprogramming to ensure the achievement of the process goals. Fuzzy logic and machine learning techniques are combined to design the tool. The method was tested on an industrial hospital laundry with satisfactory results, thus highlighting the potential of this proposal for its incorporation into the Industry 4.0 framework.

Keywords

Artificial intelligence Intelligent manufacturing Machine learning Operation management Decision support system 

Notes

Acknowledgements

José Manuel Gonzalez-Cava’s research was supported by the Spanish Ministry of Science, Innovation and Universities (http://www.ciencia.gob.es/) under the “Formación de Profesorado Universitario” Grant FPU15/03347.

Compliance with ethical standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Departamento de Ingeniería Informática y de SistemasUniversidad de La Laguna (ULL)La LagunaSpain

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