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A Sustainable Green Supply Chain Model with Carbon Emissions for Defective Items under Learning in a Fuzzy Environment

Author

Listed:
  • Basim S. O. Alsaedi

    (Department of Statistics, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Osama Abdulaziz Alamri

    (Department of Statistics, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Mahesh Kumar Jayaswal

    (Department of Mathematics and Statistics, Banasthali Vidyapith (Banasthali University), Banasthali 304022, India)

  • Mandeep Mittal

    (Department of Mathematics, Amity Institute of Applied Sciences, Amity University Uttar Pradesh, Noida 201301, India)

Abstract

Assuming the significance of sustainability, it is considered necessary to ensure the conservation of our natural resources, in addition to minimizing waste. To promote significant sustainable effects, factors including production, transportation, energy usage, product control management, etc., act as the chief supports of any modern supply chain model. The buyer performs the firsthand inspection and returns any defective items received from the customer to the vendor in a process that is known as first-level inspection. The vendor uses the policy of recovery product management to obtain greater profit. A concluding inspection is accomplished at the vendor’s end in order to distinguish the returned item as belonging to one of four specific categories, namely re-workable, reusable, recyclable, and disposable, a process that is known as second-level inspection. Then, it is observed that some defective items are suitable for a secondary market, while some are reusable, and some can be disassembled to shape new derived products, and leftovers can be scrapped at the disposal cost. This ensures that we can meet our target to promote a cleaner drive with a lower percentage of carbon emissions, reducing the adverse effects of landfills. The activity of both players in this model is presented briefly in the flowchart shown in the abstract. Thus, our aim of product restoration is to promote best practices while maintaining economic value, with the ultimate goal of removing the surrounding waste with minimum financial costs. In this regard, it is assumed that the demand rate is precise in nature. The learning effect and fuzzy environment are also considered in the present model. The proposed model studies the impacts of learning and carbon emissions on an integrated green supply chain model for defective items in fuzzy environment and shortage conditions. We optimized the integrated total fuzzy profit with respect to the order quantity and shortages. We described the vendor’s strategy and buyer’s strategy through flowcharts for the proposed integrated supply chain model, and here, in the flowchart, R-R-R stands for re-workable, reusable, and recyclable. The demand rate was treated as a triangular fuzzy number. In this paper, a numerical example, sensitivity analysis, limitations, future scope, and conclusion are presented for the validation of the proposed model.

Suggested Citation

  • Basim S. O. Alsaedi & Osama Abdulaziz Alamri & Mahesh Kumar Jayaswal & Mandeep Mittal, 2023. "A Sustainable Green Supply Chain Model with Carbon Emissions for Defective Items under Learning in a Fuzzy Environment," Mathematics, MDPI, vol. 11(2), pages 1-36, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:301-:d:1027636
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    References listed on IDEAS

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    Cited by:

    1. Basim S. O. Alsaedi, 2024. "A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment," Mathematics, MDPI, vol. 12(10), pages 1-33, May.
    2. Basim S. O. Alsaedi & Marwan H. Ahelali, 2024. "A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory," Mathematics, MDPI, vol. 12(8), pages 1-38, April.
    3. Asterios Stroumpoulis & Evangelia Kopanaki & Panos T. Chountalas, 2024. "Enhancing Sustainable Supply Chain Management through Digital Transformation: A Comparative Case Study Analysis," Sustainability, MDPI, vol. 16(16), pages 1-29, August.
    4. Lin Chen & Ting Dong & Jin Peng & Dan Ralescu, 2023. "Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review," Mathematics, MDPI, vol. 11(11), pages 1-45, May.
    5. Chunhua Tang & Erbao Cao & Zhanwen Shi, 2023. "Channel Competition and Coordination of a Dual-Channel Supply Chain under Interval Demand," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    6. Basim S. O. Alsaedi, 2024. "A Sustainable Supply Chain Model with Variable Production Rate and Remanufacturing for Imperfect Production Inventory System under Learning in Fuzzy Environment," Mathematics, MDPI, vol. 12(18), pages 1-49, September.
    7. Osama Abdulaziz Alamri, 2023. "A Supply Chain Model with Carbon Emissions and Preservation Technology for Deteriorating Items under Trade Credit Policy and Learning in Fuzzy," Mathematics, MDPI, vol. 11(13), pages 1-58, June.

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