Uta Mohring

Sustainable operations management in production, logistics and shared mobility

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: Benefits of using real-time data


Work under review


Elena Tappia, Emilio Moretti, Uta Mohring, Ivo Adan
2023

Cite

Cite

APA   Click to copy
Tappia, E., Moretti, E., Mohring, U., & Adan, I. (2023). Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: Benefits of using real-time data.


Chicago/Turabian   Click to copy
Tappia, Elena, Emilio Moretti, Uta Mohring, and Ivo Adan. “Part Feeding Scheduling for Mixed-Model Assembly Lines with Autonomous Mobile Robots: Benefits of Using Real-Time Data” (2023).


MLA   Click to copy
Tappia, Elena, et al. Part Feeding Scheduling for Mixed-Model Assembly Lines with Autonomous Mobile Robots: Benefits of Using Real-Time Data. 2023.


BibTeX   Click to copy

@article{elena2023a,
  title = {Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: Benefits of using real-time data},
  year = {2023},
  author = {Tappia, Elena and Moretti, Emilio and Mohring, Uta and Adan, Ivo}
}

Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.




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