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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
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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).
    4. 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.
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    Cited by:

    1. James Adu Peprah & John Amoah & Kofi Kwarteng & Abdul Bashiru Jibril & Taimur Sharif, 2025. "Artificial intelligence and additive manufacturing for resilient supply chain in Africa: A systematic literature review," Future Business Journal, Springer, vol. 11(1), pages 1-12, December.

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