Skip to main content
SpringerLink
Log in

Agent-Based Model Assessing the Quality of the Cyber-Physical System

  • Conference paper
  • First Online:
Advances in Automation IV (RusAutoCon 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 986))

Included in the following conference series:

  • 297 Accesses

Abstract

Currently, cyber-physical systems are actively developing. Therefore, research aimed at creating efficient systems is relevant. One of the tasks of such systems is the redistribution of functions between the human and the system. In the case of intensive activity of the employee and the accumulation of fatigue, it is necessary to support his activity with a robot. Despite the existence of some models, this issue has not been fully studied especially concerning cyber-physical systems. The work aims to develop an agent-based model for assessing the quality of the cyber-physical system, consisting of a worker performing intensive work and a robot to support the worker. The model involves the parameters of the worker and the parameters of the labor activity of the worker and robot. As a result of the work, a model was built, and modeling was performed. The simulation result is a series of curves and their analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Monostori, L.: Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17, 9–13 (2014). https://doi.org/10.1016/J.PROCIR.2014.03.115

    Article  Google Scholar 

  2. Longo, F., Nicoletti, L., Padovano, A.: Smart operators in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput. Ind. Eng. 113, 144–159 (2017). https://doi.org/10.1016/J.CIE.2017.09.016

    Article  Google Scholar 

  3. Perez, J., de Looze, M.P., Bosch, T., Neumann, W.P.: Discrete event simulation as an ergonomic tool to predict workload exposures during systems design. Int. J. Ind. Ergon. 44, 298–306 (2014). https://doi.org/10.1016/J.ERGON.2013.04.007

    Article  Google Scholar 

  4. Negahban, A., Smith, J.S.: Simulation for manufacturing system design and operation: literature review and analysis. J. Manuf. Syst. 33, 241–261 (2014). https://doi.org/10.1016/J.JMSY.2013.12.007

    Article  Google Scholar 

  5. Gräßler, I., Wiechel, D., Roesmann, D.: Integrating human factors in the model based development of cyber-physical production systems. In: Procedia CIRP, pp. 518–523. Elsevier B.V., (2021)

    Google Scholar 

  6. Xu, S., Hall, N.G.: Fatigue, personnel scheduling and operations: review and research opportunities. Eur. J. Oper. Res. 295, 807–822 (2021)

    Article  MATH  Google Scholar 

  7. Konz, S.: Work/rest: Part II—the scientific basis (knowledge base) for the guide. Int. J. Ind. Ergon. 22, 73–99 (1998). https://doi.org/10.1016/S0169-8141(97)00069-3

    Article  Google Scholar 

  8. de la Riva, J., Garcia, A.I., Reyes, R.M., Woocay, A.: Methodology to determine time allowance by work sampling using heart rate. Procedia Manuf. 3, 6490–6497 (2015). https://doi.org/10.1016/J.PROMFG.2015.07.934

    Article  Google Scholar 

  9. El ahrache, K., Imbeau, D.: Comparison of rest allowance models for static muscular work. Int. J. Ind. Ergon. 39, 73–80 (2009). https://doi.org/10.1016/J.ERGON.2008.10.012

  10. Givi, Z.S., Jaber, M.Y., Neumann, W.P.: Modelling worker reliability with learning and fatigue. Appl. Math. Model. 39, 5186–5199 (2015). https://doi.org/10.1016/J.APM.2015.03.038

    Article  MATH  Google Scholar 

  11. Jaber, M.Y., Neumann, W.P.: Modelling worker fatigue and recovery in dual-resource constrained systems. Comput. Ind. Eng. 59, 75–84 (2010). https://doi.org/10.1016/J.CIE.2010.03.001

    Article  Google Scholar 

  12. el Mouayni, I., Etienne, A., Lux, A., et al.: A simulation-based approach for time allowances assessment during production system design with consideration of worker’s fatigue, learning and reliability. Comput. Ind. Eng. 139, 105650 (2020). https://doi.org/10.1016/j.cie.2019.01.024

  13. El Mouayni, I., Demesure, G., El Haouzi, H.B., et al.: Jobs scheduling within Industry 4.0 with consideration of worker’s fatigue and reliability using Greedy Randomized Adaptive Search Procedure. In: IFAC-PapersOnLine, pp. 85–90. Elsevier B.V., (2019)

    Google Scholar 

  14. Vinod, V., Sridharan, R.: Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis. Int. J. Adv. Manuf. Technol. 36, 355–372 (2008). https://doi.org/10.1007/S00170-006-0836-4

    Article  Google Scholar 

  15. Carnahan, B.J., Redfern, M.S., Norman, B.: Designing safe job rotation schedules using optimization and heuristic search. Ergonomics 43, 543–560 (2010). https://doi.org/10.1080/001401300184404

    Article  Google Scholar 

  16. El Mouayni, I., Etienne, A., Siadat, A., et al.: AEN-PRO: Agent-based simulation tool for performance and working conditions assessment in production systems using workers’ margins of manoeuver, pp. 14236–14241. Elsevier B.V. (2017)

    Google Scholar 

  17. Ferjani, A., Ammar, A., Pierreval, H., Elkosantini, S.: A simulation-optimization based heuristic for the online assignment of multi-skilled workers subjected to fatigue in manufacturing systems. Comput. Ind. Eng. 112, 663–674 (2017). https://doi.org/10.1016/j.cie.2017.02.008

    Article  Google Scholar 

  18. Muñoz, S., Iglesias, C.A.: An agent based simulation system for analyzing stress regulation policies at the workplace. J. Comput. Sci. 51, 101326 (2021). https://doi.org/10.1016/j.jocs.2021.101326

  19. Ranz, F., Hummel, V., Sihn, W.: Capability-based task allocation in human-robot collaboration. Procedia Manuf. 9, 182–189 (2017). https://doi.org/10.1016/J.PROMFG.2017.04.011

    Article  Google Scholar 

  20. Schmidbauer, C., Schlund, S., Ionescu, T.B., Hader, B.: Adaptive task sharing in human-robot interaction in assembly. In: IEEE International Conference on Industrial Engineering and Engineering Management 2020, pp. 546–550 (2020). https://doi.org/10.1109/IEEM45057.2020.9309971

  21. Lamon, E., de Franco, A., Peternel, L., Ajoudani, A.: A capability-aware role allocation approach to industrial assembly tasks. IEEE Robot. Autom. Lett. 4, 3378–3385 (2019). https://doi.org/10.1109/LRA.2019.2926963

    Article  Google Scholar 

  22. Schmidbauer, C., Hader, B., Schlund, S.: Evaluation of a digital worker assistance system to enable adaptive task sharing between humans and cobots in manufacturing. Procedia CIRP 104, 38–43 (2021). https://doi.org/10.1016/j.procir.2021.11.007

    Article  Google Scholar 

  23. Muślewski, L., Woropay, M., Bojar, P.: The evaluation method of human–machine–environment systems operation quality. In: Engineering Asset Management and Infrastructure Sustainability, pp. 675–691 (2012). https://doi.org/10.1007/978-0-85729-493-7_52

  24. Varnavsky, A.N., Mironov, V.V.: Dependence modeling of “price/quality” of human-machine system from the ratio of the elements duration of the “work: Rest” cycle: dependence modeling of “price/quality” of human-machine system. In: 2017 6th Mediterranean Conference on Embedded Computing, MECO 2017 - Including ECYPS 2017, Proceedings (2017). https://doi.org/10.1109/MECO.2017.7977205

  25. Varnavsky, A.N.: Research of simple genetic algorithm parameters for estimating duration of the elements of the “work-rest” cycle of a production worker. In: IEEE 12th International Conference on Application of Information and Communication Technologies, AICT 2018 - Proceedings (2018). https://doi.org/10.1109/ICAICT.2018.8747071

Download references

Author information

Authors and Affiliations

  1. HSE University, 20, Myasnitskaya St, Moscow, 101000, Russia

    A. N. Varnavsky

Authors
  1. A. N. Varnavsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. N. Varnavsky .

Editor information

Editors and Affiliations

  1. Moscow Polytechnic University, Moscow, Russia

    Andrey A. Radionov

  2. Moscow Polytechnic University, Moscow, Russia

    Vadim R. Gasiyarov

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Varnavsky, A.N. (2023). Agent-Based Model Assessing the Quality of the Cyber-Physical System. In: Radionov, A.A., Gasiyarov, V.R. (eds) Advances in Automation IV. RusAutoCon 2022. Lecture Notes in Electrical Engineering, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-031-22311-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-22311-2_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22310-5

  • Online ISBN: 978-3-031-22311-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation