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Redundant configuration of robotic assembly lines with stochastic failures

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  • Christoph Müller
  • Martin Grunewald
  • Thomas Stefan Spengler

Abstract

One of the main challenges in the operation of robotic assembly lines is the occurrence of failures. Due to the connection of the stations via a material handling system, failures at one station often result in throughput losses. To some extent, these throughput losses can be reduced by installing buffers between the stations. However, the installation of buffers requires considerable investments and scarce factory space. Due to the advances of manufacturing technologies that form the foundation of ‘Industry 4.0’, new solutions to reduce failure-related throughput losses open up. One solution is a redundant configuration, in which downstream (backup) stations automatically take over the operations of failed stations during repair time. The throughput loss in these situations depends on the allocation of operations and the assignment of backup stations. Existing approaches in the literature that consider redundancies in the configuration of automated lines neglect the resulting production rate. Instead, the lines’ level of redundancy is used as a surrogate measure for optimisation. We present a genetic algorithm for the redundant configuration of robotic assembly lines with stochastic failures to maximise the production rate of the line. In a numerical analysis, it is demonstrated that this approach allows for productivity improvements.

Suggested Citation

  • Christoph Müller & Martin Grunewald & Thomas Stefan Spengler, 2018. "Redundant configuration of robotic assembly lines with stochastic failures," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3662-3682, May.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:10:p:3662-3682
    DOI: 10.1080/00207543.2017.1406672
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    Cited by:

    1. Weckenborg, Christian & Schumacher, Patrick & Thies, Christian & Spengler, Thomas S., 2024. "Flexibility in manufacturing system design: A review of recent approaches from Operations Research," European Journal of Operational Research, Elsevier, vol. 315(2), pages 413-441.
    2. Kamble, Sachin S. & Gunasekaran, Angappa & Ghadge, Abhijeet & Raut, Rakesh, 2020. "A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Christian Weckenborg & Karsten Kieckhäfer & Christoph Müller & Martin Grunewald & Thomas S. Spengler, 2020. "Balancing of assembly lines with collaborative robots," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 93-132, April.
    4. Gillani, Fatima & Chatha, Kamran Ali & Sadiq Jajja, Muhammad Shakeel & Farooq, Sami, 2020. "Implementation of digital manufacturing technologies: Antecedents and consequences," International Journal of Production Economics, Elsevier, vol. 229(C).
    5. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).

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