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.
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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.
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González Rodríguez, G., Gonzalez-Cava, J.M. & Méndez Pérez, J.A. An intelligent decision support system for production planning based on machine learning. J Intell Manuf 31, 1257–1273 (2020). https://doi.org/10.1007/s10845-019-01510-y
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DOI: https://doi.org/10.1007/s10845-019-01510-y