Abstract
The seamless link between physical and cyber components becomes more important as the industry develops and integrates concepts such as Industry 5.0. Traditionally, frameworks on Cyber-Physical Systems overlook the significance of human factors, leading to lower performance and reduced user satisfaction. This paper shows an approach of a CPS that integrates human engagement by assessing the ergonomic impact of repeated activities with an OCRA measurement. The framework proposes a dynamic control architecture that adapts task planning and workloads rules to improve both human and system performance by continually monitoring the OCRA index. This method lowers the possibility of physical strain by ensuring that jobs are assigned according to human capacities. The proposed approach was evaluated in a simulated scenario of a flexible manufacturing system using multi-agent systems paradigm.
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References
Ahangar, N.K., Khalili, M., Tayebi, H.: The three-objective optimization model of flexible workshop scheduling problem for minimizing work completion time, work delay time, and energy consumption. TECH. J. 15, 76–83 (2021)
Melesse, T.Y., Di Pasquale, V., Riemma, S.: Digital twin models in industrial operations: a systematic literature review. In: International Conference on Industry 4.0 and Smart Manufacturing, vol. 42, pp. 267–272 (2020)
de Paula Ferreira, W., Armellini, F., De Santa-Eulalia, L.A.: Simulation in industry 4.0: a state-of-the-art review. Comput. Ind. Eng. 149, 106868 (2020)
Ghobakhloo, M., Iranmanesh, M., Tseng, M.-L., Grybauskas, A., Stefanini, A., Amran, A.: Behind the definition of Industry 5.0: a systematic review of technologies, principles, components, and values. J. Ind. Prod. Eng. 1–16 (2023)
Grabowska, S., Saniuk, S., Gajdzik, B.: Industry 5.0: improving humanization and sustainability of industry 4.0. Scientometrics 127(6), 3117–3144 (2022)
Nikolakis, N., Maratos, V., Makris, S.: A cyber physical system (CPS) approach for safe human-robot collaboration in a shared workplace. Robot. Comput. Integr. Manuf. 56, 233–243 (2019)
Kotronis, C., Routis, I., Tsadimas, A., Nikolaidou, M., Anagnostopoulos, D.: A model-based approach for the design of cyber-physical human systems emphasizing human concerns. In: Proceedings - 2019 IEEE ICIOT 2019, pp. 100–107 (2019)
Ruiz Garcia, M.A., Rojas, R., Gualtieri, L., Rauch, E., Matt, D.: A human-in-the-loop cyber-physical system for collaborative assembly in smart manufacturing. Procedia CIRP 81, 600–605. Elsevier B.V. (2019)
Zhang, C., Zhou, G., Ma, D., Wang, R., Xiao, J., Zhao, D.: A deep learning-enabled human-cyber-physical fusion method towards human-robot collaborative assembly. Robot. Comput. Integr. Manuf. 83, 102571 (2023)
Benotsmane, R., Kovács, G., Dudás, L.: Economic, social impacts and operation of smart factories in industry 4.0 focusing on simulation and artificial intelligence of collaborating robots. Soc. Sci. 8(5) (2019)
Bousdekis, A., Apostolou, D., Mentzas, G.: A human cyber physical system framework for operator 4.0 – artificial intelligence symbiosis. Manuf. Lett. 25, 10–15 (2020)
Celaschi, F.: Advanced design-driven approaches for an industry 4.0 framework: the human-centred dimension of the digital industrial revolution the industry 4.0 phenomenon 1. Strateg. Des. Res. J. 10(2), 97–104 (2017)
Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Flexible job shop scheduling problem under industry 5.0: a survey on human reintegration, environmental consideration and resilience improvement. J. Manuf. Syst. 67, 155–173 (2023)
Şenyiğit, E., Atici, U., Şenol, M.B.: Effects of OCRA parameters and learning rate on machine scheduling. Cent. Eur. J. Oper. Res. 30(3), 941–959 (2022)
Antons, O., Arlinghaus, J.C.: Adaptive self-learning distributed and centralized control approaches for smart factories. Procedia CIRP 104, 1577–1582 (2021)
Blunck, H., Armbruster, D., Bendul, J., Hütt, M.-T.: The balance of autonomous and centralized control in scheduling problems. Appl. Netw. Sci. 3(1), 16 (2018)
Overmars, A.H., Toncich, D.J.: Hybrid FMS control architectures based on holonic principles. Int. J. Flex. Manuf. Syst. 8(3), 263–278 (1996)
Paredes-Astudillo, Y.A., Jimenez, J.F., Zambrano-Rey, G., Trentesaux, D.: Human-machine cooperation for the distributed control of a hybrid control architecture. Stud. Comput. Intell. 853, 98–110 (2020)
Jimenez, J.F., Bekrar, A., Zambrano-Rey, G., Trentesaux, D., Leitão, P.: Pollux: a dynamic hybrid control architecture for flexible job shop systems. Int. J. Prod. Res. 55(15), 4229–4247 (2017)
Rey, G.Z., Pach, C., Aissani, N., Bekrar, A., Berger, T., Trentesaux, D.: The control of myopic behavior in semi-heterarchical production systems: a holonic framework. Eng. Appl. Artif. Intell. 26(2), 800–817 (2013)
Pach, C., Berger, T., Sallez, Y., Trentesaux, D.: Reactive control of overall power consumption in flexible manufacturing systems scheduling: a potential fields model. Control. Eng. Pract. 44, 193–208 (2015)
Diego-Mas, J.A.: Evaluación del riesgo por movimientos repetitivos mediante el Check List Ocra, Ergonautas, Universidad Politécnica de Valencia (2015)
Occhipinti, E.: OCRA: a concise index for the assessment of exposure to repetitive movements of the upper limbs. Ergonomics 41(9), 1290–1311 (1998)
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Meza, SM., Perez, MJ., Guerra-Cruz, A., Paredes-Astudillo, Y.A., Jimenez, JF. (2024). A Framework for Enhancing Human-Agent Interaction in Cyber-Physical Systems: OCRA Measurement Perspective. In: Borangiu, T., Trentesaux, D., Leitão, P., Berrah, L., Jimenez, JF. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2023. Studies in Computational Intelligence, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-031-53445-4_20
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