IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v8y2024i4p131-d1545053.html
   My bibliography  Save this article

Application of Mixed-Integer Linear Programming Models for the Sustainable Management of Vine Pruning Residual Biomass: An Integrated Theoretical Approach

Author

Listed:
  • Leonel J. R. Nunes

    (proMetheus, Unidade de Investigação em Materiais, Energia e Ambiente para a Sustentabilidade, Instituto Politécnico de Viana do Castelo, Rua da Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal
    Escola Superior de Ciências Empresariais, Instituto Politécnico de Viana do Castelo, Rua da Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal
    DEGEIT, Departamento de Economia, Gestão, Engenharia Industrial e Turismo, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
    GOVCOPP, Unidade de Investigação em Governança, Competitividade e Políticas Públicas, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal)

Abstract

Background : This study explores the use of Mixed-Integer Linear Programming (MILP) models to optimize the collection and transportation of vineyard pruning biomass, a crucial resource for sustainable energy and material production. Efficient biomass logistics play a key role in supporting circular bioeconomy principles by improving resource utilization and reducing operational costs. Methods : Two optimization approaches are evaluated: a base MILP model designed for scenarios with single processing points and an advanced model that incorporates intermediate processing steps to enhance logistical efficiency. The models were tested using synthetic datasets simulating vineyard regions to assess their performance. Results : The models demonstrated significant improvements, achieving cost reductions of up to 30% while enhancing operational efficiency and resource utilization. The study highlights the scalability and real-world applicability of the proposed models. Conclusions : The findings underscore the potential of MILP models in optimizing biomass supply chains and advancing circular bioeconomy goals. However, key limitations, such as computational complexity and adaptability to dynamic environments, are noted. Future research should focus on real-time data integration, dynamic updates, and multi-objective optimization to improve model robustness and applicability across diverse supply chain scenarios.

Suggested Citation

  • Leonel J. R. Nunes, 2024. "Application of Mixed-Integer Linear Programming Models for the Sustainable Management of Vine Pruning Residual Biomass: An Integrated Theoretical Approach," Logistics, MDPI, vol. 8(4), pages 1-15, December.
  • Handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:131-:d:1545053
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/8/4/131/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2305-6290/8/4/131/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schniederjans, Dara G. & Curado, Carla & Khalajhedayati, Mehrnaz, 2020. "Supply chain digitisation trends: An integration of knowledge management," International Journal of Production Economics, Elsevier, vol. 220(C).
    2. Judit Nagy & Judit Oláh & Edina Erdei & Domicián Máté & József Popp, 2018. "The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary," Sustainability, MDPI, vol. 10(10), pages 1-25, September.
    3. S. Bhuvaneshwari & Hiroshan Hettiarachchi & Jay N. Meegoda, 2019. "Crop Residue Burning in India: Policy Challenges and Potential Solutions," IJERPH, MDPI, vol. 16(5), pages 1-19, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Katona & Zoltán Birkner & Erzsébet Péter, 2023. "Examining Digital Transformation Trends in Austrian and Hungarian Companies," Sustainability, MDPI, vol. 15(15), pages 1-22, August.
    2. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.
    3. Guoqing Zhao & Shaofeng Liu & Sebastian Elgueta & Juan Pablo Manzur & Carmen Lopez & Huilan Chen, 2022. "Knowledge Mobilization for Agri-Food Supply Chain Decisions: Identification of Knowledge Boundaries and Categorization of Boundary-Spanning Mechanisms," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 15(2), pages 1-25, December.
    4. Ying Song & Lu Yang & Stavros Sindakis & Sakshi Aggarwal & Charles Chen, 2023. "Analyzing the Role of High-Tech Industrial Agglomeration in Green Transformation and Upgrading of Manufacturing Industry: the Case of China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(4), pages 3847-3877, December.
    5. Margarita Išoraitė & Gintarė Gulevičiūtė & Nikolaj Ambrusevič, 2022. "Impact of Industry 4.0 on business studies," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 9(3), pages 64-75, March.
    6. Tripti Tiwari & Mohit Tiwari, 2020. "Current Economic Slowdown and Opportunities for Cost Effective Services and Products using Technological Inventions: An Empirical Study," International Journal of Economics and Financial Issues, Econjournals, vol. 10(5), pages 121-129.
    7. Di Vaio, Assunta & Palladino, Rosa & Pezzi, Alberto & Kalisz, David E., 2021. "The role of digital innovation in knowledge management systems: A systematic literature review," Journal of Business Research, Elsevier, vol. 123(C), pages 220-231.
    8. Jaroslav Vrchota & Petr Řehoř & Monika Maříková & Martin Pech, 2020. "Critical Success Factors of the Project Management in Relation to Industry 4.0 for Sustainability of Projects," Sustainability, MDPI, vol. 13(1), pages 1-19, December.
    9. Wieslaw Urban & Krzysztof Łukaszewicz & Elżbieta Krawczyk-Dembicka, 2020. "Application of Industry 4.0 to the Product Development Process in Project-Type Production," Energies, MDPI, vol. 13(21), pages 1-20, October.
    10. Renda, Andrea & Laurer, Moritz, 2020. "IoT 4 SDGs - What can the Digital Transformation and IoT achieve for Agenda 2030?," CEPS Papers 26658, Centre for European Policy Studies.
    11. Rajeev Kumar Gupta & Hitesh Hans & Anu Kalia & Jasjit Singh Kang & Jagroop Kaur & Paramjit Kaur Sraw & Anmol Singh & Abed Alataway & Ahmed Z. Dewidar & Mohamed A. Mattar, 2022. "Long-Term Impact of Different Straw Management Practices on Carbon Fractions and Biological Properties under Rice–Wheat System," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    12. Kehinde O. Olatunji & Daniel M. Madyira, 2023. "Optimization of Biomethane Yield of Xyris capensis Grass Using Oxidative Pretreatment," Energies, MDPI, vol. 16(10), pages 1-11, May.
    13. Shih-Chia Chang & Hsu-Hwa Chang & Ming-Tsang Lu, 2021. "Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
    14. Giorgia Sammarco & Daniel Ruzza & Behzad Maleki Vishkaei & Pietro De Giovanni, 2022. "The Impact of Digital Technologies on Company Restoration Time Following the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    15. Lorena Espina-Romero & Jesús Guerrero-Alcedo, 2022. "Fields Touched by Digitalization: Analysis of Scientific Activity in Scopus," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    16. Gheorghe Minculete & Sebastian Emanuel Stan & Lucian Ispas & Ioan Virca & Leontin Stanciu & Marius Milandru & Gabriel Mănescu & Mădălina-Ioana Bădilă, 2022. "Relational Approaches Related to Digital Supply Chain Management Consolidation," Sustainability, MDPI, vol. 14(17), pages 1-28, August.
    17. Oliver Kovacs, 2019. "Big IFs in Productivity-Enhancing Industry 4.0," Social Sciences, MDPI, vol. 8(2), pages 1-17, January.
    18. Gábor Szabó-Szentgróti & Bence Végvári & József Varga, 2021. "Impact of Industry 4.0 and Digitization on Labor Market for 2030-Verification of Keynes’ Prediction," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    19. Foshammer, Jeppe & Søberg, Peder Veng & Helo, Petri & Ituarte, Iñigo Flores, 2022. "Identification of aftermarket and legacy parts suitable for additive manufacturing: A knowledge management-based approach," International Journal of Production Economics, Elsevier, vol. 253(C).
    20. Seyed Amirali Hoseini & Alireza Fallahpour & Kuan Yew Wong & Amir Mahdiyar & Morteza Saberi & Serdar Durdyev, 2021. "Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches," Sustainability, MDPI, vol. 13(3), pages 1-19, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:131-:d:1545053. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.