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Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing

Author

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  • Zhongfei Zhang

    (School of Management, Jinan University, Guangzhou 510632, China
    Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China
    Institute of Physical Internet, Jinan University, Zhuhai 519070, China)

  • Ting Qu

    (Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China
    Institute of Physical Internet, Jinan University, Zhuhai 519070, China
    School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China)

  • Kuo Zhao

    (Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China
    Institute of Physical Internet, Jinan University, Zhuhai 519070, China
    School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China)

  • Kai Zhang

    (Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China
    Institute of Physical Internet, Jinan University, Zhuhai 519070, China
    Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China)

  • Yongheng Zhang

    (School of Management, Jinan University, Guangzhou 510632, China
    Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China
    Institute of Physical Internet, Jinan University, Zhuhai 519070, China)

  • Lei Liu

    (School of Management, Jinan University, Guangzhou 510632, China)

  • Jun Wang

    (Guangdong Sanpu Garage Shares Co., Ltd., Zhaoqing 526238, China)

  • George Q. Huang

    (Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics, Jinan University, Zhuhai 519070, China
    Institute of Physical Internet, Jinan University, Zhuhai 519070, China
    Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

In the quest for sustainable production, manufacturers are increasingly adopting mixed-flow production modes to meet diverse product demands, enabling small-batch production and ensuring swift delivery. A key aspect in this shift is optimizing material distribution scheduling to maintain smooth operations. However, traditional methods frequently encounter challenges due to outdated information tools, irrational task allocation, and suboptimal route planning. Such limitations often result in distribution disarray, unnecessary resource wastage, and general inefficiency, thereby hindering the economic and environmental sustainability of the manufacturing sector. Addressing these challenges, this study introduces a novel dynamic material distribution scheduling optimization model and strategy, leveraging digital twin (DT) technology. This proposed strategy aims to bolster cost-effectiveness while simultaneously supporting environmental sustainability. Our methodology includes developing a route optimization model that minimizes distribution costs, maximizes workstation satisfaction, and reduces carbon emissions. Additionally, we present a cloud–edge computing-based decision framework and explain the DT-based material distribution system’s components and operation. Furthermore, we designed a DT-based dynamic scheduling optimization mechanism, incorporating an improved ant colony optimization algorithm. Numerical experiments based on real data from a partner company revealed that the proposed material distribution scheduling model, strategy, and algorithm can reduce the manufacturer’s distribution operation costs, improve resource utilization, and reduce carbon emissions, thereby enhancing the manufacturer’s economic and environmental sustainability. This research offers innovative insights and perspectives that are crucial for advancing sustainable logistics management and intelligent algorithm design in analogous manufacturing scenarios.

Suggested Citation

  • Zhongfei Zhang & Ting Qu & Kuo Zhao & Kai Zhang & Yongheng Zhang & Lei Liu & Jun Wang & George Q. Huang, 2023. "Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin: A Step towards Sustainable Manufacturing," Sustainability, MDPI, vol. 15(23), pages 1-29, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16539-:d:1293779
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    References listed on IDEAS

    as
    1. Fang Li & Tao Li & Wen-Tsao Pan, 2022. "Intelligent Logistics Enterprise Management Based on the Internet of Things," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-7, June.
    2. Emde, Simon & Gendreau, Michel, 2016. "Scheduling in-house transport vehicles to feed parts to automotive assembly lines," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 84551, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Hao Li & Xiaocong Wang & Yan Liu & Gen Liu & Zhongshang Zhai & Xinyu Yan & Haoqi Wang & Yuyan Zhang, 2023. "A Novel Robotic-Vision-Based Defect Inspection System for Bracket Weldments in a Cloud–Edge Coordination Environment," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    4. Ilias Vlachos & Rodrigo Martinez Pascazzi & Miltiadis Ntotis & Konstantina Spanaki & Stella Despoudi & Panagiotis Repoussis, 2022. "Smart and flexible manufacturing systems using Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT)," Post-Print hal-03825237, HAL.
    5. Hu, Yue & Yang, Hongbing & Huang, Yi, 2022. "Conflict-free scheduling of large-scale multi-load AGVs in material transportation network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
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