Hierarchical Parameter Calibration of Digital Twins at Ford Motor Company

References

• Aiyoshi E, Shimizu K (1981) Hierarchical decentralized systems and its new solution by a barrier method. IEEE Trans. Systems Man Cybernetics 11(6):444–449. Google Scholar
• Anandalingam G, Friesz TL (1992) Hierarchical optimization: An introduction. Ann. Oper. Res. 34(1):1–11.Google Scholar
• Bailey WC, Che J, Tsou P, Jennings M (2018) A framework for automated model interface coordination using sysml. J. Comput. Inform. Sci. Engrg. 18(3):031010.Google Scholar
• Barlatt AY, Cohn A, Gusikhin O, Fradkin Y, Davidson R, Batey J (2012) Ford motor company implements integrated planning and scheduling in a complex automotive manufacturing environment. Interfaces (Providence) 42(5):478–491.Link, Google Scholar
• Bűrmen Á, Olenšek J, Tuma T (2015) Mesh adaptive direct search with second directional derivative-based hessian update. Comput. Optim. Appl. 62(3):693–715.Google Scholar
• Chelst K, Sidelko J, Przebienda A, Lockledge J, Mihailidis D (2001) Rightsizing and management of prototype vehicle testing at ford motor company. Interfaces (Providence) 31(1):91–107.Link, Google Scholar
• Chong A, Menberg K (2018) Guidelines for the Bayesian calibration of building energy models. Energy Building 174:527–547.Google Scholar
• Colson B, Marcotte P, Savard G (2007) An overview of bilevel optimization. Ann. Oper. Res. 153(1):235–256.Google Scholar
• Eriksson D, Poloczek M (2021) Scalable constrained Bayesian optimization. Banerjee A, Fukumizu K, eds. Proc. Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 130 (PMLR, New York), 730–738.Google Scholar
• Frazier PI (2018) A tutorial on Bayesian optimization. Preprint, submitted July 8, https://arxiv.org/abs/1807.02811.Google Scholar
• Gardner JR, Kusner MJ, Xu ZE, Weinberger KQ, Cunningham JP (2014) Bayesian optimization with inequality constraints. Xing EP, Jebara T, eds. Proc. 31st Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 32 (PMLR, New York), 937–945.Google Scholar
• Gelbart MA, Snoek J, Adams RP (2014) Bayesian optimization with unknown constraints. Preprint, submitted March 24, https://arxiv.org/abs/1403.5607.Google Scholar
• Gramacy RB, Le Digabel S (2015) The mesh adaptive direct search algorithm with treed Gaussian process surrogates. Pacific J. Optim. 11(3):419–447.Google Scholar
• Higdon D, Kennedy M, Cavendish JC, Cafeo JA, Ryne RD (2004) Combining field data and computer simulations for calibration and prediction. SIAM J. Sci. Comput. 26(2):448–466.Google Scholar
• Jain P, Shashaani S, Byon E (2025) Simulation model calibration with dynamic stratification and adaptive sampling. J. Simulation 19(5):494–515. Google Scholar
• Jang Y, Byon E, Vanage S, Cetin K, Jahn DE, Gallus W, Manuel L (2023) Spatiotemporal post-calibration in a numerical weather prediction model for quantifying building energy consumption. IEEE Trans. Automation Sci. Engrg. 20(4):2732–2747.Google Scholar
• Jeong C, Byon E (2024) Calibration of building energy computer models via bias-corrected iteratively reweighted least squares method. Appl. Energy 360:122753.Google Scholar
• Jeong C, Xu Z, Berahas AS, Byon E, Cetin K (2023) Multiblock parameter calibration in computer models. INFORMS J. Data Sci. 2(2):116–137.Link, Google Scholar
• Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492. Google Scholar
• Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J. Roy. Statist. Soc. Ser. B (Statist. Methodology) 63(3):425–464.Google Scholar
• Larson J, Menickelly M, Wild SM (2019) Derivative-free optimization methods. Acta Numerics 28:287–404.Google Scholar
• Liu B, Yue X, Byon E, Kontar RA (2022) Parameter calibration in wake effect simulation model with stochastic gradient descent and stratified sampling. Ann. Appl. Statist. 16(3):1795–1821.Google Scholar
• Močkus J (1975) On Bayesian methods for seeking the extremum. Marchuk GI, ed. Proc. Optim. Techniques IFIP Technical Conf. (Springer, Berlin, Heidelberg), 400–404.Google Scholar
• Müller J, Day M (2019) Surrogate optimization of computationally expensive black-box problems with hidden constraints. INFORMS J. Comput. 31(4):689–702.Link, Google Scholar
• Oppenheim AV, Willsky AS, Nawab SH (1997) Signals & Systems (Prentice Hall, Upper Saddle River, NJ).Google Scholar
• Park J, Byon E, Ko YM, Shashaani S (2025) Strata design for variance reduction in stochastic simulation. Technometrics 67(2):203–214.Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12:2825–2830.Google Scholar
• Raba D, Tordecilla RD, Copado P, Juan AA, Mount D (2022) A digital twin for decision making on livestock feeding. INFORMS J. Appl. Anal. 52(3):267–282.Link, Google Scholar
• Reich D, Winkler SL, Klampfl E, Olson N (2015) Ford uses analytics to help fleet customers buy more sustainable vehicles. Interfaces (Providence) 45(6):543–553.Link, Google Scholar
• Santner TJ, Williams BJ, Notz WI (2018) The Design and Analysis of Computer Experiments, 2nd ed. (Springer, Berlin).Google Scholar
• Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N (2015) Taking the human out of the loop: A review of Bayesian optimization. Proc. IEEE 104(1):148–175.Google Scholar
• Sinha A, Malo P, Deb K (2017) A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE Trans. Evolutionary Comput. 22(2):276–295.Google Scholar
• Song E, Wu-Smith P, Nelson BL (2020) Uncertainty quantification in vehicle content optimization for general motors. INFORMS J. Appl. Anal. 50(4):225–238.Link, Google Scholar
• Srinivas N, Krause A, Kakade S, Seeger M (2010) Gaussian process optimization in the bandit setting: No regret and experimental design. Fürnkranz J, Joachims T, eds. Proc. 27th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 1015–1022.Google Scholar
• Tuo R, Wu C (2015) Efficient calibration for imperfect computer models. Ann. Statist. 43(6):2331–2352.Google Scholar
• Tuo R, He S, Pourhabib A, Ding Y, Huang JZ (2023) A reproducing kernel Hilbert space approach to functional calibration of computer models. J. Amer. Statist. Assoc. 118(542):883–897.Google Scholar
• Wu-Smith P, Keenan PT, Owen JH, Norton A, Kamm K, Schumacher KM, Fenyes P, et al. (2023) General motors optimizes vehicle content for customer value and profitability. INFORMS J. Appl. Anal. 53(1):59–69.Link, Google Scholar
• Youn BD, Jung BC, Xi Z, Kim SB, Lee W (2011) A hierarchical framework for statistical model calibration in engineering product development. Comput. Methods Appl. Mechanical Engrg. 200(13–16):1421–1431.Google Scholar