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REFERENCES
Ait-Alla, A., Kreutz, M., Rippel, D., L
¨
utjen, M., and Fre-
itag, M. (2019). Simulation-based analysis of the in-
teraction of a physical and a digital twin in a cyber-
physical production system. IFAC-PapersOnLine,
52(13):1331–1336. 9th IFAC Conference on Manu-
facturing Modelling, Management and Control MIM
2019.
Borshchev, A. (2013). Anylogic 7: New release presen-
tation. In Proceedings of the 2013 Winter Simulation
Conference: Simulation: Making Decisions in a Com-
plex World, WSC ’13, page 4106. IEEE Press.
Borshchev, A. (2014). Multi-method modelling: AnyLogic,
chapter 12, pages 248–279. John Wiley & Sons, Ltd.
Continental (2023). Advanced Antenna Solutions. https:
//www.continental-automotive.com/Passenger-Cars/
Vehicle-Networking/5G-Connectivity-Solutions/
Advanced-Antenna-Solutions-(1). [Online; accessed
April 2023].
Damiani, L., Demartini, M., Giribone, P., Maggiani, M.,
Revetria, R., and Tonelli, F. (2018). Simulation and
digital twin based design of a production line: A case
study. In Proceedings of the International MultiCon-
ference of Engineers and Computer Scientists, vol-
ume 2.
DIGI2-Lab (2023). Digi2-feup/dinasore. [Online; accessed
April 2023].
Elder, M. (2014). DES view on simulation modelling:
SIMUL8, chapter 10, pages 199–214. John Wiley &
Sons, Ltd.
Glaessgen, E. and Stargel, D. (2012). The Digital Twin
Paradigm for Future NASA and U.S. Air Force Vehi-
cles, page 1818. AIAA.
Kassen, S., Tammen, H., Zarte, M., and Pechmann, A.
(2021). Concept and case study for a generic simu-
lation as a digital shadow to be used for production
optimisation. Processes, 9(8).
Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn,
W. (2018). Digital twin in manufacturing: A cat-
egorical literature review and classification. IFAC-
PapersOnLine, 51(11):1016–1022. 16th IFAC Sym-
posium on Information Control Problems in Manufac-
turing INCOM 2018.
Longo, F., Padovano, A., Nicoletti, L., Elbasheer, M., and
Diaz, R. (2021). Digital twins for manufacturing and
logistics systems: is simulation practice ready? In
Proceedings of the 33rd European Modeling & Simu-
lation Symposium (EMSS 2021), pages 435–442.
Lyu, G. and Brennan, R. W. (2021). Towards iec 61499-
based distributed intelligent automation: A literature
review. IEEE Transactions on Industrial Informatics,
17(4):2295–2306.
Nordgren, W. (2002). Flexsim simulation environment.
In Proceedings of the Winter Simulation Conference,
volume 1, pages 250–252 vol.1.
Park, H., Easwaran, A., and Andalam, S. (2019). Chal-
lenges in digital twin development for cyber-physical
production systems. In Chamberlain, R., Taha, W.,
and T
¨
orngren, M., editors, Cyber Physical Systems.
Model-Based Design, pages 28–48, Cham. Springer
International Publishing.
Pereira, E., Reis, J., and Gonc¸alves, G. (2020). Dinasore:
A dynamic intelligent reconfiguration tool for cyber-
physical production systems. In Eclipse Conference
on Security, Artificial Intelligence, and Modeling for
the Next Generation Internet of Things (Eclipse SAM
IoT), pages 63–71.
Pinheiro, J., Pinto, R., Gonc¸alves, G., and Ribeiro, A.
(2023). Lean 4.0: A digital twin approach for auto-
mated cycle time collection and yamazumi analysis.
In press on the 3rd International Conference on Elec-
trical, Computer, Communications and Mechatronics
Engineering (ICECCME).
Pinto, R., Reis, J., Silva, R., Peschl, M., and Gonc¸alves, G.
(2016). Smart sensing components in advanced manu-
facturing systems. International Journal on Advances
in Intelligent Systems, 9(1&2):181–198.
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang,
L., and Nee, A. (2021). Enabling technologies and
tools for digital twin. Journal of Manufacturing Sys-
tems, 58:3–21. Digital Twin towards Smart Manufac-
turing and Industry 4.0.
Rossetti, M. D. (2015). Simulation modeling and Arena.
John Wiley & Sons.
Schwarz, M. H. and B
¨
orcs
¨
ok, J. (2013). A survey on opc
and opc-ua: About the standard, developments and
investigations. In 2013 XXIV International Confer-
ence on Information, Communication and Automation
Technologies (ICAT), pages 1–6.
Shukla, O. J., Soni, G., and Kumar, R. (2019). Simula-
tion modeling for manufacturing system application
using simulink/simevents. In Bansal, J. C., Das, K. N.,
Nagar, A., Deep, K., and Ojha, A. K., editors, Soft
Computing for Problem Solving, pages 751–760, Sin-
gapore. Springer Singapore.
Singgih, I. K. (2021). Production flow analysis in a
semiconductor fab using machine learning techniques.
Processes, 9(3).
Vik, P., Dias, L., Pereira, G., Oliveira, J., and Abreu, R.
(2010). Using simio for the specification of an inte-
grated automated weighing solution in a cement plant.
In Proceedings of the 2010 Winter Simulation Confer-
ence, pages 1534–1546.
Wolfe-Adam, T. (2023). AnyLogic-Pypeline. https://github.
com/t-wolfeadam/AnyLogic-Pypeline. [Online; ac-
cessed April 2023].
Zhang, L., Zhou, L., Ren, L., and Laili, Y. (2019). Modeling
and simulation in intelligent manufacturing. Comput-
ers in Industry, 112:103123.
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