2,160
Views
20
CrossRef citations to date
0
Altmetric
Research Article

Real-time digital twin-based optimization with predictive simulation learning

, , ORCID Icon &
Pages 47-64 | Received 13 Sep 2021, Accepted 13 Feb 2022, Published online: 07 Mar 2022

References

  • Bastani, M., Damgacioglu, H., & Celik, N. (2018). A δ-constraint multi-objective optimization framework for operation planning of smart grids. Sustainable Cities and Society, 38, 21–30. https://doi.org/10.1016/j.scs.2017.12.006
  • Bastani, M., Thanos, A. E., Damgacioglu, H., Celik, N., & Chen, C.-H. (2018). An evolutionary simulation optimization framework for interruptible load management in the smart grid. Sustainable Cities and Society, 41, 802–809. https://doi.org/10.1016/j.scs.2018.06.007
  • Branke, J., Chick, S. E., & Schmidt, C. (2007). Selecting a selection procedure. Management Science, 53(12), 1916–1932. https://doi.org/10.1287/mnsc.1070.0721
  • Brantley, M. W., Lee, L. H., Chen, C.-H., & Xu, J. (2014). An efficient simulation budget allocation method incorporating regression for partitioned domains. Automatica, 50(5), 1391–1400. https://doi.org/10.1016/j.automatica.2014.03.011
  • Calverley, J., Currie, C., Onggo, B. S., Monks, T., & Higgins, M. (2021). Simulation optimisation for improving the efficiency of a production line. In Proceedings of the Operational Research Society Simulation Workshop 2021 (SW21) M. Fakhimi, D. Robertson, and T. Boness, eds. pp. 137–144. https://doi.org/10.36819/SW21.014.
  • Cao, Yiyun, Currie, Christine, Onggo, Bhakti Stephan, and Higgins, Michael. (2021). Simulation optimization for a digital twin using a multi-fidelity framework. Proceedings of the 2021 Winter Simulation Conference, Phoenix, AZ. December 12-15, 2021, IEEE. DOI: 10.1109/WSC52266.2021.9715498
  • Celik, N., & Son, Y.-J. (2012). Sequential monte carlo-based fidelity selection in dynamic- data-driven adaptive multi-scale simulations. International Journal of Production Research, 50(3), 843–865. https://doi.org/10.1080/00207543.2010.545445
  • Chen, C.-H., & Lee, L. H. (2010). Stochastic simulation optimization: An optimal computing budget allocation. World scientific.
  • Chen, C.-H., Lin, J., Yücesan, E., & Chick, S. E. (2000). Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems, 10(3), 251–270. https://doi.org/10.1023/A:1008349927281
  • Chen, T., & Wang, C. (2016). Multi-objective simulation optimization for medical capacity allocation in emergency department. Journal of Simulation, 10(1), 50–68. https://doi.org/10.1057/jos.2014.39
  • Chick, S. E., Branke, J., & Schmidt, C. (2010). Sequential sampling to myopically maximize the expected value of information. INFORMS Journal on Computing, 22(1), 71–80. https://doi.org/10.1287/ijoc.1090.0327
  • Damgacioglu, H., & Celik, N. (2022). A two-stage decomposition method for integrated optimization of islanded ac grid operation scheduling and network reconfiguration. International Journal of Electrical Power &. Energy Systems, 136, 107647. https://doi.org/10.1016/j.ijepes.2021.107647
  • Damgacioglu, H., Celik, E., & Celik, N. (2018). Intra-cluster distance minimization in DNA methylation analysis using an advanced tabu-based iterative k k-medoids clustering algorithm (t-clust). IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4), 1241–1252. https://doi.org/10.1109/TCBB.2018.2886006
  • Damgacioglu, H., Celik, E., & Celik, N. (2019). Estimating gene expression from high- dimensional DNA methylation levels in cancer data: A bimodal unsupervised dimension reduction algorithm. Computers & Industrial Engineering, 130, 348–357. https://doi.org/10.1016/j.cie.2019.02.038
  • Darville, J., & Celik, N. (2020a). Microgrid operational planning using deviation clustering within a dddas framework. In International conference on dynamic data driven application systems, Boston, MA, USA, ACM. (pp. 77–84). https://doi.org/10.36819/SW21.014.
  • Darville, J., & Celik, N. (2020b). Simulation and optimization for unit commitment using a regionbased sampling (rbs) algorithm. In Iie annual conference. proceedings (pp. 1424–1430). New Orleans, LA, IISE.
  • Du, M., Liu, N., & Hu, X. (2019). Techniques for interpretable machine learning. Communications of the ACM, 63(1), 68–77. https://doi.org/10.1145/3359786
  • Eckman, D. J., & Henderson, S. G. (2021). Fixed-confidence, fixed-tolerance guarantees for ranking-and-selection procedures. ACM Transactions on Modeling and Computer Simulation (TOM A CS), 31(2), 1–33. https://doi.org/10.1145/3432754
  • Frazier, P. I., Powell, W. B., & Dayanik, S. (2008). A knowledge-gradient policy for sequential information collection. SIAM Journal on Control and Optimization, 47(5), 2410–2439. https://doi.org/10.1137/070693424
  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. h ttps://d oi.org/1 0.1214/aos/1176347963
  • Fu, M. C. (2015). Handbook of simulation optimization. Springer.
  • Gao, S., Chen, W., & Shi, L. (2017). A new budget allocation framework for the expected opportunity cost. Ope Rations Research, 65(3), 787–803. https://doi.org/10.1287/opre.2016.1581
  • Gao, S., Du, J., & Chen, C.-H. (2019b). Selecting the optimal system design under covariates. In 2019 ieee 15th international conference on automation science and engineering (case) (pp. 547–552).Vancouver, Canada, IEEE.
  • Griewank, A. O. (1981). Generalized descent for global optimization. Journal of Optimization Theory and Applications, 34(1), 11–39. https://doi.org/10.1007/BF00933356
  • He, D., Chick, S. E., & Chen, C.-H. (2007). Opportunity cost and ocba selection procedures in ordinal optimization for a fixed number of alternative systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and R Eviews), 37(5), 951–961. https://doi.org/10.1109/TSMCC.2007.900656
  • Hong, J. L., Fan, W., & Luo, J. (2021). Review on ranking and selection: A new perspective. Frontiers of Engineering Management, 8(3), 321–343. https://doi.org/10.1007/s42524-021-0152-6
  • Hong, L. J., & Jiang, G. (2019). Offline simulation online application: A new framework of simulation-based decision making. Asia-Pacific Journal of Ope Rational Research, 36(6), 1940015. https://doi.org/10.1142/S0217595919400153
  • Hsieh, B.-W., Chen, C.-H., & Chang, S.-C. (2007). Efficient simulation-based composition of scheduling policies by integrating ordinal optimization with design of experiment. IEEE Transactions on Automation Science and Engineering, 4(4), 553–568. https://doi.org/10.1109/TASE.2007.906342
  • Jiang, G., Hong, L. J., & Nelson, B. L. (2020). Online risk monitoring using offline simulation. INFORMS Journal on Computing, 32(2), 356–375. https://doi.org/10.1287/ijoc.2019.0892
  • Kasaie, P., & Kelton, W. D. (2013). Simulation optimization for allocation of epidemic-control resources. IIE Transactions on Healthcare Systems Engineering, 3(2), 78–93. https://doi.org/10.1080/19488300.2013.788102
  • Kim, S.-H., & Nelson, B. 2006. Selecting the best system. In S. H. SG, and B. Nelson (Eds.), Elsevier hand books in operations research and management science: Simulation. Elsevier. Chapter 17 p.
  • LaPorte, G. J., Branke, J., & Chen, C.-H. (2012). Optimal computing budget allocation for small computing budgets. In Proceedings of the 2012 winter simulation conference (wsc) (pp. 1–13).Berlin, Germany, IEEE.
  • LaPorte, G. J., Branke, J., & Chen, C.-H. (2015). Adaptive parent population sizing in evolution strategies. Evolutionary Computation, 23(3), 397–420. https://doi.org/10.1162/EVCO_a_00136
  • Lin, Y., Nelson, B. L., & Pei, L. (2019). Virtual statistics in simulation via k nearest neighbors. INFORMS Journal on Computing, 31(3), 576–592. https://doi.org/10.1287/ijoc.2018.0839
  • Luo, J., Hong, L. J., Nelson, B. L., & Wu, Y. (2015). Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments. Operations Research, 63(5), 1177–1194. https://doi.org/10.1287/opre.2015.1413
  • Molnar, C. (2020). Interpretable machine learning. Lulu. com.
  • Ni, E. C., Ciocan, D. F., Henderson, S. G., & Hunter, S. R. (2017). Efficient ranking and selection in parallel computing environments. Operations Research, 65(3), 821–836. https://doi.org/10.1287/opre.2016.1577
  • Pearce, M., & Branke, J. (2017). Efficient expected improvement estimation for continuous multiple ranking and selection. In 2017 winter simulation conference (wsc) (pp. 2161–2172).Las Vegas, NV, USA, IEEE.
  • Pedrielli, G., Selcuk Candan, K., Chen, X., Mathesen, L., Inanalouganji, A., Xu, J., … Lee, L. H. (2019). Generalized ordinal learning framework (golf) for decision making with future simulated data. Asia-Pacific Journal of Operational Research, 36(6), 1940011. https://doi.org/10.1142/S0217595919400116
  • Peng, Y., Xu, J., Lee, L. H., Hu, J., & Chen, C.-H. (2019). Efficient simulation sampling allocation using multifidelity models. IEEE Transactions on A Utomatic Control, 64(8), 3156–3169. https://doi.org/10.1109/TAC.2018.2886165
  • Pickardt, C., Branke, J., Hildebrandt, T., Heger, J., & Scholz-Reiter, B. (2010). Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness. In Proceedings of the 2010 winter simulation conference (pp. 2504–2515). Baltimore, MD, USA, IEEE.
  • Qiu, Y., & Song, J. (2016). A multiobjective simulation optimization of the macrolevel patient flow distribution. Healthcare Analytics, 303.Wiley. h ttps://d oi.org/1 0.1002/9781118919408.ch10
  • Ryzhov, I. O., Powell, W. B., & Frazier, P. I. (2012). The knowledge gradient algorithm for a general class of online learning problems. Operations Research, 60(1), 180–195. https://doi.org/10.1287/opre.1110.0999
  • Santner, T. J., Williams, B. J., Notz, W. I., & Williams, B. J. (2018). The design and analysis of computer experiments (Vol. 2). Springer.
  • Shen, H., Hong, L. J., & Zhang, X. (2021). Ranking and selection with covariates for personalized decision making. INFORMS Journal on Computing, 33(4). https://doi.org/10.1287/ijoc.2020.1009
  • Shi, X., & Celik, N. (2016). Relative entropy-based density selection in particle filtering for load demand forecast. IEEE Transactions on Automation Science and Engineering, 14(2), 946–954. https://doi.org/10.1109/TASE.2016.2552221
  • Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., & Van Den Driessche, G. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489. others https://doi.org/10.1038/nature16961
  • Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., & Guez, A. (2017). Mastering the game of go without human knowledge. Nature, 550(7676), 354–359. others https://doi.org/10.1038/nature24270
  • Song, J., Qiu, Y., Xu, J., & Yang, F. (2019). Multi-fidelity sampling for efficient simulation- based decision making in manufacturing management. IISE Transactions, 51(7), 792–805. https://doi.org/10.1080/24725854.2019.1576951
  • Taghiyeh, S., & Xu, J. (2016). A new particle swarm optimization algorithm for noisy optimization problems. Swarm Intelligence, 10(3), 161–192. https://doi.org/10.1007/s11721-016-0125-2
  • Thanos, A. E., Bastani, M., Celik, N., & Chen, C.-H. (2015). Dynamic data driven adaptive simulation framework for automated control in microgrids. IEEE Transactions on Smart Grid, 8(1), 209–218. https://doi.org/10.1109/TSG.2015.2464709
  • Xu, J., Huang, E., Hsieh, L., Lee, L. H., Jia, Q.-S., & Chen, C.-H. (2016). Simulation optimization in the era of industrial 4.0 and the industrial internet. Journal of Simulation, 10(4), 310–320. https://doi.org/10.1057/s41273-016-0037-6
  • Xu, J., Vidyashankar, A., & Nielsen, M. (2014). Drug resistance or re-emergence? simulating equine parasites. ACM Transactions on Modeling and Computer Simulation, 24(4), 201–2023. https://doi.org/10.1145/2627736
  • Xu, J., Yao, R., & Qiu, F. (2020). Mitigating cascading outages in severe weather using simulation-based optimization. IEEE Transactions on Rer Systems, 36(1), 204–213. https://doi.org/10.1109/TPWRS.2020.3008428
  • Yavuz, A., Darville, J., Celik, N., Xu, J., Chen, C.-H., Langhals, B., & Engle, R. (2020). Advancing self-healing capabilities in interconnected microgrids via dynamic data driven applications system with relational database management. In 2020 winter simulation conference (wsc) (pp. 2030–2041). IEEE.
  • Zhang, F., Song, J., Dai, Y., & Xu, J. (2020). Semiconductor wafer fabrication production planning using multi-fidelity simulation optimisation. International Journal of Production Research, 58(21), 6585–6600. https://doi.org/10.1080/00207543.2019.1683252
  • Zhong, Y., & Hong, J. (2021). Knockout-tournament procedures for large-scale ranking and selection in parallel computing environments. Operations Research, 70(1), 432–453. https://doi.org/10.1287/opre.2020.2065
  • Zhong, Y., Liu, S., Luo, J., & Hong, L. J. (2021). Speeding up paulson’s procedure for large-scale problems using parallel computing. INFORMS Journal on R Computing. https://doi.org/10.1287/ijoc.2020.1054
  • Zhou, C., Xu, J., Miller-Hooks, E., Zhou, W., Chen, C.-H., Lee, L. H., & Li, H. (2021). Analytics with digital-twinning: A decision support system for maintaining a resilient port. Decision Support Systems, 143, 113496. https://doi.org/10.1016/j.dss.2021.113496
  • Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.