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A Grey-Fuzzy Programming Approach towards Socio-Economic Optimization of Second-Generation Biodiesel Supply Chains

Author

Listed:
  • Mohammad Kanan

    (Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia)

  • Muhammad Salman Habib

    (Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Anam Shahbaz

    (Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Amjad Hussain

    (Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Tufail Habib

    (Department of Industrial Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Hamid Raza

    (Department of Chemistry, University of Management and Technology, C-II Phase 1 Johar Town, Lahore 54770, Pakistan)

  • Zaher Abusaq

    (Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia)

  • Ramiz Assaf

    (Department of Industrial Engineering, An-Najah National University, Nablus P.O. Box 7, Palestine)

Abstract

This study aims to develop a multi-objective second-generation-based socially responsible supply chain (SGB-SRSC) network design model that considers all dimensions of sustainability: economy, environment, and social. The dynamic nature of the biodiesel supply chain (SC) impairs the SGB-SRSC model decisions; thus, a grey-fuzzy solution approach is developed. Biodiesel is a promising renewable energy resource produced from a variety of easily accessible domestic wastes. For a swift transition towards commercially feasible biodiesel production, integrated optimization of the biodiesel SC system is critical. Using the latest social impact assessment tools, this study provides a decision-support system for developing a biodiesel SC network. A comprehensive computational analysis is performed on a case study to validate the proposed model. The results show that significant investment is required to achieve social well-being goals and secure decisions against uncertainty associated with SGB-SRSC model parameters. Further, it is observed that the expenses of biodiesel production and biodiesel plant installation accounted for a large portion of the overall SC cost. As a result, finding more cost-effective biodiesel production methods is critical to the industry’s economic viability. Regulators and policymakers involved in biodiesel production projects may find the framework useful in obtaining a compromise solution for socio-economic goals based on their preferences.

Suggested Citation

  • Mohammad Kanan & Muhammad Salman Habib & Anam Shahbaz & Amjad Hussain & Tufail Habib & Hamid Raza & Zaher Abusaq & Ramiz Assaf, 2022. "A Grey-Fuzzy Programming Approach towards Socio-Economic Optimization of Second-Generation Biodiesel Supply Chains," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10169-:d:889556
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    References listed on IDEAS

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    Cited by:

    1. Sotiris Lycourghiotis, 2025. "GIS and Spatial Analysis in the Utilization of Residual Biomass for Biofuel Production," J, MDPI, vol. 8(2), pages 1-12, May.
    2. Zaher Abusaq & Sadaf Zahoor & Muhammad Salman Habib & Mudassar Rehman & Jawad Mahmood & Mohammad Kanan & Ray Tahir Mushtaq, 2023. "Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study," Energies, MDPI, vol. 16(4), pages 1-15, February.
    3. Mohammad Kanan & Muhammad Salman Habib & Tufail Habib & Sadaf Zahoor & Anas Gulzar & Hamid Raza & Zaher Abusaq, 2022. "A Flexible Robust Possibilistic Programming Approach for Sustainable Second-Generation Biogas Supply Chain Design under Multiple Uncertainties," Sustainability, MDPI, vol. 14(18), pages 1-32, September.
    4. Habib, Muhammad Salman & Hwang, Seung-June, 2024. "Developing sustainable, resilient, and responsive biofuel production and distribution management system: A neutrosophic fuzzy optimization approach based on artificial intelligence and geographic info," Applied Energy, Elsevier, vol. 372(C).

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