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Scheduling mobile robots in part feeding systems

In: Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31

Author

Listed:
  • Morett, Emilio
  • Tappia, Elena
  • Melacini, Marco

Abstract

Purpose: Industry 4.0 has increased the availability of real-time data in manufacturing systems, but scientific evidence about the value stemming from such data is still lacking in several fields. This paper studies data-driven approaches for the assignment of tasks to a fleet of mobile robots transporting parts to the stations of a mixed model assembly line. The approaches exploit real-time data concerning the robots and assembly stations state. Methodology: An agent-based simulation model of the system, including factory warehouses, assembly stations, and robots, is developed and validated through a real case in the automotive industry. Findings: The paper proposes a model that measures the part feeding system performance in terms of transportation tasks completion time, idle time of the assembly stations due to lack of materials, and amount of inventories at the assembly line. Different data-driven approaches are considered, differing among each other for the type of real-time data used and for the update frequency of the task assignment. Originality: The developed model enriches the ones presented in previous literature by including new information (e.g., robots failures) and new data-driven approaches, such as the dynamic assignment of tasks to robots.

Suggested Citation

  • Morett, Emilio & Tappia, Elena & Melacini, Marco, 2021. "Scheduling mobile robots in part feeding systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 129-149, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:249614
    DOI: 10.15480/882.3979
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    References listed on IDEAS

    as
    1. Boysen, Nils & Emde, Simon & Hoeck, Michael & Kauderer, Markus, 2015. "Part logistics in the automotive industry: Decision problems, literature review and research agenda," European Journal of Operational Research, Elsevier, vol. 242(1), pages 107-120.
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    4. Boysen, Nils & Emde, Simon & Hoeck, Michael & Kauderer, Markus, 2015. "Part logistics in the automotive industry: Decision problems, literature review and research agenda," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 79443, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Ting Qu & Matthias Thürer & Junhao Wang & Zongzhong Wang & Huan Fu & Congdong Li & George Q. Huang, 2017. "System dynamics analysis for an Internet-of-Things-enabled production logistics system," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2622-2649, May.
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    Advanced Manufacturing; Industry 4.0;

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