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Multi-robot multi-operator collaborative assembly systems: a performance evaluation model

Author

Listed:
  • Giovanni Boschetti

    (University of Padova)

  • Matteo Bottin

    (University of Padova)

  • Maurizio Faccio

    (University of Padova)

  • Riccardo Minto

    (University of Padova)

Abstract

In the last decade, collaborative assembly systems (CAS) are becoming increasingly common due to their ability to merge the flexibility of a manual assembly system with the performance of traditional robotics. Technical constraints, e.g., dedicated tools or resources, or performance requirements, e.g., throughput, could encourage the use of a CAS built around a multi-robot and multi-operator layout, i.e., with a number of resources greater than 2. Starting from the development of a prototype multi-robot multi-operator collaborative workcell, a simulation environment was developed to evaluate the makespan and the degree of collaboration in multi-robot multi-operator CAS. From the simulation environment, a mathematical model was conceptualized. The presented model allows estimating, with a certain degree of accuracy, the performances of the system. The results have investigated how several process characteristics, i.e. the number and type of resources, the resources layout, the task allocation method, and the number of feeding devices, influence the degree of collaboration between the resources. Lastly, the authors propose a compact analytic formulation, based on an exponential function, and define the methods and the influence factors to determine its parameters.

Suggested Citation

  • Giovanni Boschetti & Matteo Bottin & Maurizio Faccio & Riccardo Minto, 2021. "Multi-robot multi-operator collaborative assembly systems: a performance evaluation model," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1455-1470, June.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:5:d:10.1007_s10845-020-01714-7
    DOI: 10.1007/s10845-020-01714-7
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    References listed on IDEAS

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    1. Abdelhamid Boudjelida, 2019. "On the robustness of joint production and maintenance scheduling in presence of uncertainties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1515-1530, April.
    2. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    3. Timo Bänziger & Andreas Kunz & Konrad Wegener, 2020. "Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1635-1648, October.
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    Cited by:

    1. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.

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