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

Author

Listed:
  • Germán González Rodríguez

    (Universidad de La Laguna (ULL))

  • Jose M. Gonzalez-Cava

    (Universidad de La Laguna (ULL))

  • Juan Albino Méndez Pérez

    (Universidad de La Laguna (ULL))

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.

Suggested Citation

  • Germán González Rodríguez & Jose M. Gonzalez-Cava & Juan Albino Méndez Pérez, 2020. "An intelligent decision support system for production planning based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1257-1273, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01510-y
    DOI: 10.1007/s10845-019-01510-y
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    References listed on IDEAS

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    Cited by:

    1. Lu-jun Cui & Man-ying Sun & Yan-long Cao & Qi-jian Zhao & Wen-han Zeng & Shi-rui Guo, 2021. "A novel tolerance geometric method based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 799-821, March.
    2. Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    3. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    4. Jindai Zhang & Jinlou Zhao, 2022. "Prediction-Driven Sequential Optimization for Refined Oil Production-Sales-Stock Decision-Making," Energies, MDPI, vol. 15(12), pages 1-19, June.
    5. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    6. Zhujun Wang & Xuyuan Tao & Xianyi Zeng & Yingmei Xing & Zhenzhen Xu & Pascal Bruniaux, 2023. "A Machine Learning-Enhanced 3D Reverse Design Approach to Personalized Garments in Pursuit of Sustainability," Sustainability, MDPI, vol. 15(7), pages 1-21, April.
    7. Yuan Li & William J. Kettinger, 2022. "Testing the Relationship Between Information and Knowledge in Computer-Aided Decision-Making," Information Systems Frontiers, Springer, vol. 24(6), pages 1827-1843, December.

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