SIMULAÇÃO HÍBRIDA NO CONTEXTO DA INDÚSTRIA 4.0: UM ESTUDO DE ESCOPO
DOI:
https://doi.org/10.56238/arev8n4-018Palavras-chave:
Simulação Híbrida, Simulação de Eventos Discretos, Simulação Baseada em Agentes, Dinâmica de Sistemas, Indústria 4.0Resumo
As técnicas de simulação podem ser utilizadas de forma integrada em aplicações complexas e no contexto da Indústria 4.0. Dentre essas técnicas, destacam-se a simulação de eventos discretos, a modelagem e simulação baseada em agentes e a dinâmica de sistemas. Esta pesquisa tem como objetivo geral identificar, por meio de um estudo de escopo, os vínculos, aplicações, características e tendências da simulação híbrida no contexto da Indústria 4.0. Para tanto, a metodologia fundamenta-se na perspectiva hipotético-dedutiva, com abordagem de pesquisa qualitativa e com a utilização do procedimento de estudo de escopo (scoping study). A pesquisa envolveu a análise de vinte e seis estudos que abordaram diferentes áreas, como manufatura, cadeias de suprimentos, sistemas energéticos e áreas urbanas, com foco na implementação de modelos híbridos que combinam metodologias de simulação discretas, contínuas e baseadas em agentes. Os principais resultados revelam que a simulação híbrida é uma ferramenta versátil e eficaz para o monitoramento, controle e otimização de sistemas complexos, promovendo maior precisão e agilidade na tomada de decisão. Destaca-se, ainda, que essa técnica facilita a criação de gêmeos digitais (digital twins), importantes para a gestão inteligente de recursos e processos na Indústria 4.0. As conclusões indicam que a integração de diferentes modelos de simulação pode constituir uma estratégia central para apoiar a inovação tecnológica, promover a sustentabilidade industrial e fortalecer a competitividade das empresas na era digital, oferecendo soluções adaptáveis às demandas de um ambiente cada vez mais dinâmico e complexo.
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Referências
ABO-KHALIL, A. G. Digital twin real-time hybrid simulation platform for power system stability. Case Studies in Thermal Engineering, v. 49, 2023. DOI https://doi.org/10.1016/j.csite.2023.103237. Disponível em: https://www.sciencedirect.com/science/article/pii/S2214157X23005439. Acesso em: 27 ago. 2025.
AL-SUBAIHAWI, S.; RICLES, J. M.; QUIEL, S. E. Online explicit model updating of nonlinear viscous dampers for real time hybrid simulation. Soil Dynamics and Earthquake Engineering, v. 154, 2022. DOI https://doi.org/10.1016/j.soildyn.2021.107108. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0267726121005303. Acesso em: 27 ago. 2025.
ARKSEY, H.; O’MALLEY, L. Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, v. 8, n. 1, p. 19-32, 2005. DOI https://doi.org/10.1080/1364557032000119616. Disponível em: https://www.tandfonline.com/doi/abs/10.1080/1364557032000119616. Acesso em: 27 ago. 2025.
AZUCENA. J. et al. Hybrid simulation to support interdependence modeling of a multimodal transportation network. Simulation Modelling Practice and Theory, v. 107, 2021. DOI https://doi.org/10.1016/j.simpat.2020.102237. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S1569190X20301696. Acesso em: 27 ago. 2025.
BAUR, C.; WEE, D. Manufacturing’s next act. McKinsey Quarterly. 2015. Disponível em: <https://www.mckinsey.com/business-functions/operations/ourinsights/manufacturings-next-act>. Acesso em: 29 jan. 2018.
BRYMAN, A. Research methods and organization studies. London: Uniwin Hyman, 1989.
CAMARGO, M. et al. Learning business process simulation models: A Hybrid process mining and deep learning approach. Information Systems, v. 117, 2023. DOI https://doi.org/10.1016/j.is.2023.102248. Disponível em: https://www.sciencedirect.com/science/article/pii/S0306437923000844. Acesso em: 27 ago. 2025.
CARRAMIÑANA, D. et al. Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making. Simulation Modelling Practice and Theory, v. 133, 2024. DOI https://doi.org/10.1016/j.simpat.2024.102924. Disponível em: https://www.sciencedirect.com/science/article/pii/S1569190X24000388. Acesso em: 27 ago. 2025.
CARVALHO, M. C. M. Construindo o saber. 2.ed. Campinas, SP: Papirus, 2000.
CAVATA, J. T. et al. Highlighting the benefits of Industry 4.0 for production: an agent-based simulation approach. Gestão & produção, 2020. DOI https://doi.org/10.1590/0104-530X5619-20. Disponível em: https://www.scielo.br/j/gp/a/H9r8h3vZcWt5pMhJLvDRkmx/?format=html&lang=en .
COVI, P. et al. Seismic experimental analysis of a full-scale steel building with passive fire protections. Engineering Structures, v. 300, 2024. DOI https://doi.org/10.1016/j.engstruct.2023.117203. Disponível em: https://www.sciencedirect.com/science/article/pii/S0141029623016188. Acesso em: 27 ago. 2025.
CRESWELL, J. W. Research design: qualitative & quantitative approaches. London: Sage, 1994.
DIESTE, O.; PADUA, A. G. Developing Search Strategies for Detecting Relevant Experiments for Systematic Reviews. In: INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT, 1., 2007. Proceedings [...]. Madrid: ESEM, 2007, p. 215224. Disponível em: https://ieeexplore.ieee.org/document/4343749/. Acesso em: 27 ago. 2025.
ELDABI, T. et al. Hybrid simulation: historical lessons, present challenges and futures. In: WINTER SIMULATION CONFERENCE, 2016. Proceedings [...]. Washington: IEEE, 2016, p. 1388-1403. Disponível em: https://ieeexplore.ieee.org/document/7822192. Acesso em: 27 ago. 2025.
FERRARI, A. et al. A Roadmap towards an Automated Warehouse Digital Twin: current implementations and future developments. IFAC-PapersOnLine, v. 55, p. 1899-1905, 2022. DOI https://doi.org/10.1016/j.ifacol.2022.09.676. Disponível em: https://www.sciencedirect.com/science/article/pii/S2405896322019942. Acesso em: 27 ago. 2025.
FERREIRA, W. P.; ARMELLINI, F.; SANTA-EULALIA, L. A. Simulation in industry 4.0: a state-of-the-art review. Computers & Industrial Engineering, v. 149, n. 1, p. 1-17, nov. 2020. DOI https://doi.org/10.1016/j.cie.2020.106868. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0360835220305635 . Acesso em: 27 ago. 2025.
FISCHER, A. et al. Cyclic Update of Project Scheduling by Using Telematics Data. IFAC-PapersOnLine, v. 54, p. 217-222, 2021. DOI https://doi.org/10.1016/j.ifacol.2021.08.025. Disponível em: https://www.sciencedirect.com/science/article/pii/S2405896321007321. Acesso em: 27 ago. 2025.
GANGWAR, S. et al. Scheduling optimization and risk analysis for energyintensive industries under uncertain electricity market to facilitate financial planning. Computers & Chemical Engineering, v. 174, 2023. DOI https://doi.org/10.1016/j.compchemeng.2023.108234. Disponível em: https://www.sciencedirect.com/science/article/pii/S0098135423001047. Acesso em: 27 ago. 2025.
GERBERT, P. et al. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industr ies. 2015. Disponível em: < https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries.aspx>. Acesso em: 29 jan. 2018.
GHASEMI, A. et al. Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap. Journal of Industrial Information Integration, mar. 2024. DOI https://doi.org/10.1016/j.jii.2024.100599. Disponível em: https://www.sciencedirect.com/science/article/pii/S2452414X24000438. Acesso em: 27 ago. 2025.
GOLROUDBARY, S. R. et al. Sustainable Operations Management in Logistics Using Simulations and Modelling: A Framework for Decision Making in Delivery Management. Procedia Manufacturing, v. 30, p. 627-634, 2019. DOI https://doi.org/10.1016/j.promfg.2019.02.088. Disponível em: https://www.sciencedirect.com/science/article/pii/S2351978919301209. Acesso em: 27 ago. 2025.
HEINZL, B. et al. Simulation-based Assessment of Energy Efficiency in Industry: Comparison of Hybrid Simulation Approaches. IFACPapersOnLine, v. 51, p. 689-694, 2018. DOI https://doi.org/10.1016/j.ifacol.2018.03.117. Disponível em: https://www.sciencedirect.com/science/article/pii/S2405896318301216. Acesso em: 27 ago. 2025.
HILLIER, F. S.; LIEBERMAN, G. J. Introdução à pesquisa operacional. Porto Alegre: AMGH, 2013.
HOFMANN, E.; RÜSCH, M. Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, v. 89, p. 23–34, ago. 2017. DOI https://doi.org/10.1016/j.compind.2017.04.002. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0166361517301902. Acesso em: 27 ago. 2025.
KITCHENHAM, B. Procedures for Performing Systematic Reviews. Joint Technical Report, Department of Computer Science, Keele University (TR/SE-0401) and National ICT Australia Ltd. (0400011T.1), 2004.
LASI, H.; FETTKE, P.; FELD, T.; HOFFMANN, M. Industry 4.0. Business & Information Systems Engineering, v. 6, n. 4, p. 239-242, 2014. DOI https://doi.org/10.1007/s12599-014-0334-4. Disponível em: https://link.springer.com/article/10.1007/s12599-014-0334-4. Acesso em: 29 jan. 2018.
LEI, R. et al. Simulation modeling of the counterfeit threat and countermeasures in ICT manufacturing supply chains. Manufacturing Letters, v. 35, p. 105-116, 2023. DOI https://doi.org/10.1016/j.mfglet.2023.08.101. Disponível em: https://www.sciencedirect.com/science/article/pii/S2213846323001621. Acesso em: 27 ago. 2025.
LEVAC, D.; COLQUHOUN, H.; O’BRIEN, K. K. Scoping studies: advancing the methodology. Implementation Science, v. 5, n. 69, p. 1-9, 2010. DOI https://doi.org/10.1186/1748-5908-5-69. Disponível em: https://link.springer.com/article/10.1186/1748-5908-5-69. Acesso em: 27 ago. 2025.
LI, H. et al. Sliding mode control design for the benchmark problem in realtime hybrid simulation. Mechanical Systems and Signal Processing, v. 151, 2021. DOI https://doi.org/10.1016/j.ymssp.2020.107364. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0888327020307500. Acesso em: 27 ago. 2025.
LUCAČ, D. The fourth ICT-based Industrial Revolution “Industry 4.0”- HMI and the case of CAE/CAD innovation with EPLAN P8. Proceedings…Belgrade : TELFOR IEEE, 2015. p. 835-838. Disponível em: https://ieeexplore.ieee.org/iel7/7368809/7377376/07377595.pdf. Acesso em: 29 jan. 2018.
MENEGHELLO, F. et al. Runtime integration of machine learning and simulation for business processes: Time and decision mining predictions. Information Systems, v. 128, 2025. DOI https://doi.org/10.1016/j.is.2024.102472. Disponível em: https://www.sciencedirect.com/science/article/pii/S0306437924001303. Acesso em: 27 ago. 2025.
MOKHTARI, F; IMANPOUR, A. A digital twin-based framework for multi-element seismic hybrid simulation of structures. Mechanical Systems and Signal Processing, v. 186, mar. 2023. DOI https://doi.org/10.1016/j.ymssp.2022.109909. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0888327022009773. Acesso em: 27 ago. 2025.
MUERZA, V.; URCIUOLI, L.; HABAS, S. Z. Enabling the circular economy of biosupply chains employing integrated biomass logistics centers - A multi-stage approach integrating supply and production activities. Journal of Cleaner Production, v. 384, 2023. DOI https://doi.org/10.1016/j.jclepro.2022.135628. Disponível em: https://www.sciencedirect.com/science/article/pii/S0959652622052027. Acesso em: 27 ago. 2025.
MUŠIČ , G.; SAGAWA, J. K. Closed-loop workload input–output control of production systems: A hybrid simulation study. Computers & Industrial Engineering, v. 198, 2024. DOI https://doi.org/10.1016/j.cie.2024.110669. Disponível em: https://www.sciencedirect.com/science/article/pii/S0360835224007915. Acesso em: 27 ago. 2025.
NGUYEN, L. K. N.; HOWICK, S.; MEGIDDO, I. A framework for conceptualising hybrid system dynamics and agent-based simulation models. European Journal of Operational Research, v. 315, p. 1153-1166, 2024. DOI https://doi.org/10.1016/j.ejor.2024.01.027. Disponível em: https://www.sciencedirect.com/science/article/pii/S0377221724000468. Acesso em: 27 ago. 2025.
PAWSON, R. Evidence-based policy: in search of a method. Evaluation, v. 8, n. 2, p. 157-181, 2002. DOI https://doi.org/10.1177/13589020020080025. Disponível em: https://journals.sagepub.com/doi/10.1177/1358902002008002512. Acesso em: 27 ago. 2025.
RODRIGUE, D. et al. Topology reduction through machine learning to accelerate dynamic simulation of district heating. Energy and AI, v. 17, 2024. DOI https://doi.org/10.1016/j.egyai.2024.100393. Disponível em: https://www.sciencedirect.com/science/article/pii/S2666546824000594. Acesso em: 27 ago. 2025.
SCHEIDEGGER, A. P. G. et al. An introductory guide for hybrid simulation modelers on the primary simulation methods in industrial engineering identified through a systematic review of the literature. Computers & Industrial Engineering, v. 124, p. 474-492, out. 2018. DOI https://doi.org/10.1016/j.cie.2018.07.046. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0360835218303693. Acesso em: 27 ago. 2025.
SILVA, E. R. et al. Plug & Produce robot assistants as shared resources: A simulation approach. Journal of Manufacturing Systems, v. 63, p. 107-117, 2022. DOI https://doi.org/10.1016/j.jmsy.2022.03.004. Disponível em: https://www.sciencedirect.com/science/article/pii/S0278612522000383. Acesso em: 27 ago. 2025.
SOBOTTKA, T. et al. Hybrid simulation-based optimization of discrete parts manufacturing to increase energy efficiency and productivity. Procedia Manufacturing, v. 21, p. 413-420, 2018. DOI https://doi.org/10.1016/j.promfg.2018.02.139. Disponível em: https://www.sciencedirect.com/science/article/pii/S2351978918301781. Acesso em: 27 ago. 2025.
TAKEUCHI, T. et al. Scaled dynamic loading tests on seismic isolation bearing excluding the contamination of friction and inertia forces. Engineering Structures, v. 296, 2023. DOI https://doi.org/10.1016/j.engstruct.2023.116844. Disponível em: https://www.sciencedirect.com/science/article/pii/S0141029623012592. Acesso em: 27 ago. 2025.
TISSOT, G. et al. A hybrid simulation/optimization architecture for developing a digital twin. IFAC-Papers online, p. 532-537, 2022. DOI https://doi.org/10.1016/j.ifacol.2022.09.448. Disponível em: https://www.sciencedirect.com/science/article/pii/S2405896322017384. Acesso em: 27 ago. 2025.
TSOKANAS, N. et al. Model order reduction for real-time hybrid simulation: Comparing polynomial chaos expansion and neural network methods. Mechanism and Machine Theory, v. 178, 2022. DOI https://doi.org/10.1016/j.mechmachtheory.2022.105072. Disponível em: https://www.sciencedirect.com/science/article/pii/S0094114X22003172. Acesso em: 27 ago. 2025.
TSOKANAS, N.; PASTORINO, R.; STOJADINOVIĆ, B. Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation. Mechanism and Machine Theory, v. 172, 2022. DOI https://doi.org/10.1016/j.mechmachtheory.2022.104817. Disponível em: https://www.sciencedirect.com/science/article/pii/S0094114X22000878. Acesso em: 27 ago. 2025.
TUR, M. et al. Hardware-in-the-Loop pantograph tests with general overhead contact line geometry. Mechatronics, v. 102, 2024. DOI https://doi.org/10.1016/j.mechatronics.2024.103231. Disponível em: https://www.sciencedirect.com/science/article/pii/S0957415824000965. Acesso em: 27 ago. 2025.
WITTEVEEN, W.; KOLLER, L.; PENNINGER, D. Non-simultaneous real-time hybrid simulation of a numerical and experimental mechanical system with moderate nonlinearities via iterative coupling based on Frequency Response Functions. Mechanical Systems and Signal Processing, v. 163, 2022. DOI https://doi.org/10.1016/j.ymssp.2021.108055. Disponível em: https://www.sciencedirect.com/science/article/pii/S0888327021004441. Acesso em: 27 ago. 2025.
XANTHOPOULOS, A.; KOSTAVELIS, I. Novel Simulation Optimization Approach for Supply Chain Coordination and Management. Procedia Computer Science, v. 232, p. 1646-1653, 2024. DOI https://doi.org/10.1016/j.procs.2024.01.162. Disponível em: https://www.sciencedirect.com/science/article/pii/S1877050924001637. Acesso em: 27 ago. 2025.
YANG, L. et al. Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs). Technological Forecasting and Social Change, v. 188, mar. 2023. DOI https://doi.org/10.1016/j.techfore.2022.122308. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0040162522008290. Acesso em: 27 ago. 2025.
ZÚÑIGA, E. R. et al. An integrated discrete-event simulation with functional resonance analysis and work domain analysis methods for industry 4.0 implementation. Decision Analytics Journal, set. 2023. DOI https://doi.org/10.1016/j.dajour.2023.100323. Disponível em: https://www.sciencedirect.com/science/article/pii/S2772662223001637. Acesso em: 27 ago. 2025.
