4.6 Article

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

期刊

ANNALS OF OPERATIONS RESEARCH
卷 308, 期 1-2, 页码 125-143

出版社

SPRINGER
DOI: 10.1007/s10479-020-03526-7

关键词

Autonomous mobile robots; Artificial Intelligence; Cloud manufacturing; Production network; Production line; Performance; Flexibility; Industry 4; 0

资金

  1. DigiMat (Research Council project) [296686]
  2. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [873077]
  3. NTNU Norwegian University of Science and Technology (St. Olavs Hospital-Trondheim University Hospital)

向作者/读者索取更多资源

This study develops and tests an analytical model for throughput analysis of autonomous mobile robots (AMR)-based flexible production networks, revealing the conditions under which they are more advantageous compared to traditional production lines. Through circular loop and sensitivity analysis, key factors in improving flexibility and productivity are identified.
Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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