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Effect of Learning and Forgetting on Inventory Model under Carbon Emission and Agile Manufacturing

Author

Listed:
  • Vandana

    (Department of Mathematics, Inderprastha Engineering College, Ghaziabad 201010, India)

  • Shiv Raj Singh

    (Department of Mathematics, Chaudhary Charan Singh University, Meerut 250001, India)

  • Mitali Sarkar

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77, Cheongam-ro, Nam-gu, Pohang-si 37673, Republic of Korea)

  • Biswajit Sarkar

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, Republic of Korea
    Center for Transdisciplinary Research (CFTR), Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, 162, Poonamallee High Road, Velappanchavadi, Chennai 600077, India)

Abstract

The aim of this study is to examine the learning and forgetting effect on a manufacturer’s production process with volume agility and carbon emission costs. During COVID-19, the learning rate becomes very low, and the forgetting rate becomes very high. That is why, the analysis of the learning and forgetting effects on the production process is very important. This research finds an effect of learning and forgetting on the manufacturer and on reducing the unit manufacturing cost. Here, the production rate is a function of the number of units produced, and it is taken as a decision variable through agile manufacturing. Here, the Weibull deterioration rate is used, and the production process is subject to the learn–forget–learn policy. Here, a carbon emission cost is introduced into the setup/ordering cost, holding cost, and item cost for the manufacturer. The effect of learning and forgetting is analyzed through numerical examples.

Suggested Citation

  • Vandana & Shiv Raj Singh & Mitali Sarkar & Biswajit Sarkar, 2023. "Effect of Learning and Forgetting on Inventory Model under Carbon Emission and Agile Manufacturing," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:368-:d:1031283
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    References listed on IDEAS

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    Cited by:

    1. Bożena Zwolińska & Jakub Wiercioch, 2023. "Modelling the Reliability of Logistics Flows in a Complex Production System," Energies, MDPI, vol. 16(24), pages 1-22, December.
    2. Amrina Kausar & Ahmad Hasan & Chandra K. Jaggi, 2023. "Sustainable inventory management for a closed-loop supply chain with learning effect and carbon emission under the multi-shipment policy," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1738-1755, October.
    3. Saha, Subrata & Sarkar, Biswajit & Sarkar, Mitali, 2023. "Application of improved meta-heuristic algorithms for green preservation technology management to optimize dynamical investments and replenishment strategies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 209(C), pages 426-450.

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