570
G. Filippi and M. Vasile
10. H.G. Beyer, B. Sendhoff, Robust optimization - a comprehensive survey. Comput. Methods
Appl. Mech. Eng. 196, 3190–3218 (2007). https://doi.org/10.1016/j.cma.2007.03.003
11. E. Zio, Reliability engineering: old problems and new challenges. Reliab. Eng. Syst. Saf. 94,
125–141 (2009). https://doi.org/10.1016/j.ress.2008.06.002
12. G. Punzo, A. Tewari, E. Butans, M. Vasile, A. Purvis, M. Mayfield, et al., Engineering resilient
complex systems: the necessary shift toward complexity science. IEEE Syst. J. 14, 3865–3874
(2020). https://doi.org/10.1109/jsyst.2019.2958829
13. S.N. Naghshbandi, L. Varga, A. Purvis, R. Mcwilliam, E. Minisci, M. Vasile, et al., A review
of methods to study resilience of complex engineering and engineered systems. IEEE Access
44, 11 (2020). https://doi.org/10.1109/access.2020.2992239
14. C.N. Calvano, P. John, Systems engineering in an age of complexity. Syst. Eng. 7, 25–34
(2004). https://doi.org/10.1002/sys.10054
15. S.A. Sheard, A. Mostashari, Principles of complex systems for systems engineering. Syst.
Eng. 12, 295–311 (2009). https://doi.org/10.1002/sys.20124
16. W. Yao, X. Chen, W. Luo, M. van Tooren, J. Guo, Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Prog. Aerosp. Sci. 47, 450–479
(2011). https://doi.org/10.1016/J.PAEROSCI.2011.05.001
17. J.C. Helton, J.D. Johnson, W.L. Oberkampf, C.J. Sallaberry, Representation of analysis results
involving aleatory and epistemic uncertainty. Int. J. Gen. Syst. 39, 605–646 (2010). https://
doi.org/10.1080/03081079.2010.486664
18. A.P. Dempster, Upper and lower probabilities induced by a multivalued mapping. Ann. Math.
Statist. 38(2), 325–339 (1967)
19. G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)
20. M. Vasile, E. Minisci, F. Zuiani, E. Komninou, Q. Wijnands, Fast evidence-based space
system engineering, in IAC (2011)
21. C.O. Absil, G. Filippi, A. Riccardi, M. Vasile, A variance-based estimation of the resilience
indices in the preliminary design optimisation of engineering systems under epistemic
uncertainty, in EUROGEN (2017)
22. J.C. Helton, J.D. Johnson, W.L. Oberkampf, C.B. Storlie, A sampling-based computational
strategy for the representation of epistemic uncertainty in model predictions with evidence
theory. Comput. Methods Appl. Mech. Eng. 196, 3980–3998 (2007). https://doi.org/10.1016/
j.cma.2006.10.049.
23. W. Yao, X. Chen, Y. Huang, Z. Gurdal, M. Van Tooren, Sequential optimization and mixed
uncertainty analysis method for reliability-based optimization. AIAA J. 51, 2266–2277,
(2013). https://doi.org/10.2514/1.J052327
24. H. Agarwal, J.E. Renaud, E.L. Preston, D. Padmanabhan, Uncertainty quantification using
evidence theory in multidisciplinary design optimization. Reliab. Eng. Syst. Saf. 85, 28194
(2004). https://doi.org/10.1016/J.RESS.2004.03.017
25. Z.P. Mourelatos, J. Zhou, A design optimization method using evidence theory. J. Mech. Des.
Trans. ASME 128, 901–908 (2006). https://doi.org/10.1115/1.2204970
26. P. Pedersen, C.L. Laureen, Design for minimum stress concentration by finite elements
and linear programming. J. Struct. Mech. 10, 375–391 (1982). https://doi.org/10.1080/
03601218208907419
27. M. Nicolich, G. Cassio, System models simulation process manangement and collaborative
multidisciplinary optimization, in CEUR Workshop Proceedings, Rome, vol. 1300 (2014), pp.
1–16
28. G.J. Park, T.H. Lee, K.H. Lee, K.H. Hwang, Robust design: an overview. AIAA J. 44, 181–
191 (2006). https://doi.org/10.2514/1.13639
29. M. Kalsi, K. Hacker, K. Lewis, A comprehensive robust design approach for decision tradeoffs in complex systems design. J. Mech. Des. Trans. ASME 123, 1–10 (2001). https://doi.
org/10.1115/1.1334596
30. X. Du, W. Chen, Towards a better understanding of modeling feasibility robustness in
engineering design. J. Mech. Des. Trans. ASME 122, 385–394 (2000). https://doi.org/10.
1115/1.1290247
G. Filippi and M. Vasile
10. H.G. Beyer, B. Sendhoff, Robust optimization - a comprehensive survey. Comput. Methods
Appl. Mech. Eng. 196, 3190–3218 (2007). https://doi.org/10.1016/j.cma.2007.03.003
11. E. Zio, Reliability engineering: old problems and new challenges. Reliab. Eng. Syst. Saf. 94,
125–141 (2009). https://doi.org/10.1016/j.ress.2008.06.002
12. G. Punzo, A. Tewari, E. Butans, M. Vasile, A. Purvis, M. Mayfield, et al., Engineering resilient
complex systems: the necessary shift toward complexity science. IEEE Syst. J. 14, 3865–3874
(2020). https://doi.org/10.1109/jsyst.2019.2958829
13. S.N. Naghshbandi, L. Varga, A. Purvis, R. Mcwilliam, E. Minisci, M. Vasile, et al., A review
of methods to study resilience of complex engineering and engineered systems. IEEE Access
44, 11 (2020). https://doi.org/10.1109/access.2020.2992239
14. C.N. Calvano, P. John, Systems engineering in an age of complexity. Syst. Eng. 7, 25–34
(2004). https://doi.org/10.1002/sys.10054
15. S.A. Sheard, A. Mostashari, Principles of complex systems for systems engineering. Syst.
Eng. 12, 295–311 (2009). https://doi.org/10.1002/sys.20124
16. W. Yao, X. Chen, W. Luo, M. van Tooren, J. Guo, Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles. Prog. Aerosp. Sci. 47, 450–479
(2011). https://doi.org/10.1016/J.PAEROSCI.2011.05.001
17. J.C. Helton, J.D. Johnson, W.L. Oberkampf, C.J. Sallaberry, Representation of analysis results
involving aleatory and epistemic uncertainty. Int. J. Gen. Syst. 39, 605–646 (2010). https://
doi.org/10.1080/03081079.2010.486664
18. A.P. Dempster, Upper and lower probabilities induced by a multivalued mapping. Ann. Math.
Statist. 38(2), 325–339 (1967)
19. G. Shafer, A Mathematical Theory of Evidence (Princeton University Press, Princeton, 1976)
20. M. Vasile, E. Minisci, F. Zuiani, E. Komninou, Q. Wijnands, Fast evidence-based space
system engineering, in IAC (2011)
21. C.O. Absil, G. Filippi, A. Riccardi, M. Vasile, A variance-based estimation of the resilience
indices in the preliminary design optimisation of engineering systems under epistemic
uncertainty, in EUROGEN (2017)
22. J.C. Helton, J.D. Johnson, W.L. Oberkampf, C.B. Storlie, A sampling-based computational
strategy for the representation of epistemic uncertainty in model predictions with evidence
theory. Comput. Methods Appl. Mech. Eng. 196, 3980–3998 (2007). https://doi.org/10.1016/
j.cma.2006.10.049.
23. W. Yao, X. Chen, Y. Huang, Z. Gurdal, M. Van Tooren, Sequential optimization and mixed
uncertainty analysis method for reliability-based optimization. AIAA J. 51, 2266–2277,
(2013). https://doi.org/10.2514/1.J052327
24. H. Agarwal, J.E. Renaud, E.L. Preston, D. Padmanabhan, Uncertainty quantification using
evidence theory in multidisciplinary design optimization. Reliab. Eng. Syst. Saf. 85, 28194
(2004). https://doi.org/10.1016/J.RESS.2004.03.017
25. Z.P. Mourelatos, J. Zhou, A design optimization method using evidence theory. J. Mech. Des.
Trans. ASME 128, 901–908 (2006). https://doi.org/10.1115/1.2204970
26. P. Pedersen, C.L. Laureen, Design for minimum stress concentration by finite elements
and linear programming. J. Struct. Mech. 10, 375–391 (1982). https://doi.org/10.1080/
03601218208907419
27. M. Nicolich, G. Cassio, System models simulation process manangement and collaborative
multidisciplinary optimization, in CEUR Workshop Proceedings, Rome, vol. 1300 (2014), pp.
1–16
28. G.J. Park, T.H. Lee, K.H. Lee, K.H. Hwang, Robust design: an overview. AIAA J. 44, 181–
191 (2006). https://doi.org/10.2514/1.13639
29. M. Kalsi, K. Hacker, K. Lewis, A comprehensive robust design approach for decision tradeoffs in complex systems design. J. Mech. Des. Trans. ASME 123, 1–10 (2001). https://doi.
org/10.1115/1.1334596
30. X. Du, W. Chen, Towards a better understanding of modeling feasibility robustness in
engineering design. J. Mech. Des. Trans. ASME 122, 385–394 (2000). https://doi.org/10.
1115/1.1290247