IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5948-d1110871.html
   My bibliography  Save this article

Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory

Author

Listed:
  • Yuran Jin

    (School of Business Administration, University of Science and Technology Liaoning, Anshan 114051, China)

  • Cheng Gao

    (School of Business Administration, University of Science and Technology Liaoning, Anshan 114051, China)

Abstract

Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.

Suggested Citation

  • Yuran Jin & Cheng Gao, 2023. "Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5948-:d:1110871
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5948/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5948/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Frank Werner & Marina Ivanova, 2016. "A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 386-402, January.
    2. Gong, Qiguo & Chen, Guohui & Zhang, Wen & Wang, Hui, 2022. "The role of humans in flexible smart factories," International Journal of Production Economics, Elsevier, vol. 254(C).
    3. Guiliang Gong & Raymond Chiong & Qianwang Deng & Qiang Luo, 2020. "A memetic algorithm for multi-objective distributed production scheduling: minimizing the makespan and total energy consumption," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1443-1466, August.
    4. Xiang, Liu, 2022. "A large-scale equilibrium model of energy emergency production: Embedding social choice rules into Nash Q-learning automatically achieving consensus of urgent recovery behaviors," Energy, Elsevier, vol. 259(C).
    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. Sony, Michael & Naik, Subhash, 2020. "Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model," Technology in Society, Elsevier, vol. 61(C).
    2. Kuang-Sheng Liu & Ming-Hung Lin, 2021. "Performance Assessment on the Application of Artificial Intelligence to Sustainable Supply Chain Management in the Construction Material Industry," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    3. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    4. Rihab Khemiri & Khaoula Elbedoui-Maktouf & Bernard Grabot & Belhassen Zouari, 2017. "A fuzzy multi-criteria decision making approach for managing performance and risk in integrated procurement-production planning," Post-Print hal-01758604, HAL.
    5. Vasja Roblek & Maja Meško & Alojz Krapež, 2016. "A Complex View of Industry 4.0," SAGE Open, , vol. 6(2), pages 21582440166, June.
    6. Hubert Wojciechowski & Michał Adamczak, 2021. "Influence of the Characteristics of Young Logisticians on the Level of Acceptance of Work in an Automated and Robotic Environment – A Survey Study," European Research Studies Journal, European Research Studies Journal, vol. 0(2B), pages 893-903.
    7. Shoujing Zhang & Tiantian Hou & Qing Qu & Adam Glowacz & Samar M. Alqhtani & Muhammad Irfan & Grzegorz Królczyk & Zhixiong Li, 2022. "An Improved Mayfly Method to Solve Distributed Flexible Job Shop Scheduling Problem under Dual Resource Constraints," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    8. De Giovanni, Pietro, 2021. "Smart Supply Chains with vendor managed inventory, coordination, and environmental performance," European Journal of Operational Research, Elsevier, vol. 292(2), pages 515-531.
    9. Tygran Dzhuguryan & Agnieszka Deja, 2021. "Sustainable Waste Management for a City Multifloor Manufacturing Cluster: A Framework for Designing a Smart Supply Chain," Sustainability, MDPI, vol. 13(3), pages 1-25, February.
    10. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    11. Cuesta-Valiño, Pedro & Gutiérrez-Rodríguez, Pablo & Núnez-Barriopedro, Estela & García-Henche, Blanca, 2023. "Strategic orientation towards digitization to improve supermarket loyalty in an omnichannel context," Journal of Business Research, Elsevier, vol. 156(C).
    12. Wen Zhang & Kee-Hung Lai & Qiguo Gong, 2024. "The future of the labor force: higher cognition and more skills," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    13. Wen-Hsien Tsai & Po-Yuan Chu & Hsiu-Li Lee, 2019. "Green Activity-Based Costing Production Planning and Scenario Analysis for the Aluminum-Alloy Wheel Industry under Industry 4.0," Sustainability, MDPI, vol. 11(3), pages 1-20, February.
    14. Frank, Alejandro G. & Mendes, Glauco H.S. & Ayala, Néstor F. & Ghezzi, Antonio, 2019. "Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 341-351.
    15. Jang Hee Lee & Olga A. Shvetsova, 2019. "The Impact of VR Application on Student’s Competency Development: A Comparative Study of Regular and VR Engineering Classes with Similar Competency Scopes," Sustainability, MDPI, vol. 11(8), pages 1-26, April.
    16. Marco Savastano & Carlo Amendola & Francesco Bellini & Fabrizio D’Ascenzo, 2019. "Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review," Sustainability, MDPI, vol. 11(3), pages 1-38, February.
    17. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    18. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    19. Hong-Long Chen, 2021. "Impact of Industry 4.0 on Corporate Financial Performance: A Moderated Mediation Model," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    20. Müller, Julian Marius & Buliga, Oana & Voigt, Kai-Ingo, 2018. "Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 2-17.

    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:jsusta:v:15:y:2023:i:7:p:5948-:d:1110871. 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.