IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i7d10.1007_s10845-018-1411-1.html
   My bibliography  Save this article

Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions

Author

Listed:
  • Timo Bänziger

    (Smart Production Lab)

  • Andreas Kunz

    (ETH Zürich)

  • Konrad Wegener

    (ETH Zürich)

Abstract

Human–robot collaboration is enabled by the digitization of production and has become a key technology for the factory of the future. It combines the strengths of both the human worker and the assistant robot and allows the implementation of an varying degree of automation in workplaces in order to meet the increasing demand of flexibility of manufacturing systems. Intelligent planning and control algorithms are needed for the organization of the work in hybrid teams of humans and robots. This paper introduces an approach to use standardized work description for automated procedure generation of mobile assistant robots. A simulation tool is developed that implements the procedure model and is therefore capable of calculating different objective parameters like production time or ergonomics during a production cycle as a function of the human–robot task allocation. The simulation is validated with an existing workplace in an assembly line at the Volkswagen plant in Wolfsburg, Germany. Furthermore, a new method is presented to optimize the task allocation in human–robot teams for a given workplace, using the simulation as fitness function in a genetic algorithm. The advantage of this new approach is the possibility to evaluate different distributions of the tasks, while considering the dynamics of the interaction between the worker and the robot in their shared workplace. Using the presented approach for a given workplace, an optimized human–robot task allocation is found, in which the tasks are allocated in an intelligent and comprehensible way.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1411-1
    DOI: 10.1007/s10845-018-1411-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1411-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-018-1411-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Izabela Nielsen & Quang-Vinh Dang & Grzegorz Bocewicz & Zbigniew Banaszak, 2017. "A methodology for implementation of mobile robot in adaptive manufacturing environments," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1171-1188, June.
    2. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2021. "Scheduling Human-Robot Teams in collaborative working cells," International Journal of Production Economics, Elsevier, vol. 235(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    2. Maximilian Zarte & Agnes Pechmann & Isabel L. Nunes, 2022. "Problems, Needs, and Challenges of a Sustainability-Based Production Planning," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    3. Congcong Ye & Jixiang Yang & Han Ding, 2022. "Bagging for Gaussian mixture regression in robot learning from demonstration," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 867-879, March.
    4. Shanhe Lou & Yixiong Feng & Hao Zheng & Yicong Gao & Jianrong Tan, 2020. "Data-driven customer requirements discernment in the product lifecycle management via intuitionistic fuzzy sets and electroencephalogram," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1721-1736, October.
    5. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    6. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    7. Wang, Di & He, Bin & Hu, Zhimu, 2024. "Financial technology and firm productivity: Evidence from Chinese listed enterprises," Finance Research Letters, Elsevier, vol. 63(C).
    8. Julian Senoner & Torbjørn Netland & Stefan Feuerriegel, 2022. "Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing," Management Science, INFORMS, vol. 68(8), pages 5704-5723, August.
    9. Vedpal Arya & S. G. Deshmukh & Naresh Bhatnagar, 2019. "Product quality in an inclusive manufacturing system: some considerations," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2871-2884, December.
    10. Senthil Sundaramoorthy & Dipti Kamath & Sachin Nimbalkar & Christopher Price & Thomas Wenning & Joseph Cresko, 2023. "Energy Efficiency as a Foundational Technology Pillar for Industrial Decarbonization," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    11. Barbara Aquilani & Michela Piccarozzi & Tindara Abbate & Anna Codini, 2020. "The Role of Open Innovation and Value Co-creation in the Challenging Transition from Industry 4.0 to Society 5.0: Toward a Theoretical Framework," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    12. Giuseppe Fragapane & Dmitry Ivanov & Mirco Peron & Fabio Sgarbossa & Jan Ola Strandhagen, 2022. "Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics," Annals of Operations Research, Springer, vol. 308(1), pages 125-143, January.
    13. Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    14. Guo, Daqiang & Li, Mingxing & Lyu, Zhongyuan & Kang, Kai & Wu, Wei & Zhong, Ray Y. & Huang, George Q., 2021. "Synchroperation in industry 4.0 manufacturing," International Journal of Production Economics, Elsevier, vol. 238(C).
    15. Ning Ge & Guanghao Li & Li Zhang & Yi Liu, 2022. "Failure prediction in production line based on federated learning: an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2277-2294, December.
    16. Shiguang Li & Yixiang Tian, 2023. "How Does Digital Transformation Affect Total Factor Productivity: Firm-Level Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    17. Farzam Farbiz & Mohd Salahuddin Habibullah & Brahim Hamadicharef & Tomasz Maszczyk & Saurabh Aggarwal, 2023. "Knowledge-embedded machine learning and its applications in smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2889-2906, October.
    18. Yuming Zhang & Han Liu & Shuang Li & Chao Xing, 2023. "The Digital Transformation Effect in Trade Credit Uptake: The Buyer Perspective," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(7), pages 2056-2078, May.
    19. Wei Fang & Lianyu Zheng, 2020. "Shop floor data-driven spatial–temporal verification for manual assembly planning," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 1003-1018, April.
    20. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1411-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.