My research on digital twin/computer simulation focuses on stochastic optimization algorithms to optimize the design and operations of a system modeled by a digital twin/simulation model. In general, a digital twin/computer simulation is a computationally expensive and stochastic black box, the key challenges are to develop algorithms that are convergent and computationally efficient.


Digital-twin based optimization
•  Goodwin, T., Xu, J., Chen, C.-H., and Celik, N. Efficient Simulation Optimization with Simulation Learning. In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). IEEE.
•  Yavuz, A., Darville, J., Celik, N., Xu, J., Chen, C.H., Langhals, B. and Engle, R., 2020, December. 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.
•  Zhou, C., Xu, J., Miller-Hooks, E., Zhou, W., Chen, C.-H., Lee, L.-H., Chew, E.-P., and Li, H., 2021. Analytics with digital-twinning: A decision support system for maintaining a resilient port. Decision Support Systems, 143 (113496) Download
•  Xu, J., Yao, R., and Qiu, F. 2021. Mitigating Cascading Outages in Severe Weather Using Simulation-based Optimization. IEEE Transactions on Power Systems, 36(1), pp.204-213. Download


Discrete optimization via Simulation using Industrial Strength COMPASS (ISC). ISC is a discrete optimization via simulation solver that has been used by many researchers in both academia and industry. ISC can be downloaded here.
•  Rosen, S., P. Salemi, B. Wickam, A. Williams, C. Harvey, E. Catlett, S. Taghiyeh, and J. Xu. Parallel Empirical Stochastic Branch and Bound for Large-scale Discrete Optimization via Simulation. Proceedings of 2016 Winter Simulation Conference, 626-637.
•  Xu, J., Nelson, B.L., and Hong, L.J. 2013. An Adaptive Hyperbox Algorithm for Discrete Optimization via Simulation. INFORMS Journal on Computing, 25:133-146. Download paper and appendix
•  Xu, J. 2012. Efficient Discrete Optimization via Simulation Using Stochastic Kriging. In Proc. of 2012 Winter Simulation Conference, pp. 466-477.
•  Hong, L.J, Nelson, B.L., Xu, J. 2011. Speeding Up COMPASS for High-Dimensional Discrete Optimization via Simulation. Operations Research Letters, 38(6) 550-555. Download
•  Xu, J., Nelson, B.L., Hong, L. J. 2010. Industrial Strength COMPASS: A Comprehensive Algorithm and Software for Optimization via Simulation, ACM Transactions on Modeling and Computer Simulation, 20(1). Download


Multi-fidelity simulation optimization
•  Peng, Y., Xu, J., Lee, L.H., Hu, J. and Chen, C.H., 2019. Efficient Simulation Sampling Allocation Using Multi-fidelity Models. IEEE Transactions on Automatic Control, 64(8), pp.3156-3169 Download
•  Song, J., Qiu, Y., Xu, J. and Yang, F., 2019. Multi-fidelity sampling for efficient simulation-based decision making in manufacturing management. IISE Transactions, 51(7), pp.792-805. Honorable Mention for the Best Paper in the 2020 IISE Transactions Focus Issue on Design and Manufacturing from all papers published from July 1, 2019 through June 30, 2020, issues 51:7 through 52:6. Download
•  Xu, J., S. Zhang, E. Huang, C.-H. Chen, L.-H. Lee, N. Celik. 2016. MOTOS: Multi-fidelity optimization via ordinal transformation and optimal sampling. Asia-Pacific Journal of Operational Research, 33 (3), 26 pages. Download
•  Xu, J., Zhang, S., Huang, E., Chen, C.-H., Lee, L.H., and Celik, N.. 2014. Efficient Multi-Fidelity Simulation Optimization. In Proc. of 2014 Winter Simulation Conference.


Optimal computing budget allocation.
•  Li, Y., Fu, M. C., and Xu, J., 2021. An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree Search. Accepted, IEEE Transactions on Automatic Control. Download
•  Wang, T., Xu, J., and Hu, J.-Q. 2021. A Study on Efficient Computing Budget Allocation for a Two-Stage Problem. Asia-Pacific Journal of Operational Research, 38(2), Article No. 2050044, 20 pages. Download
•  Peng, Y., Song, J., Xu, J. and Chong, E.K., 2020. Stochastic Control Framework for Determining Feasible Alternatives in Sampling Allocation. IEEE Transactions on Automatic Control, 65(6), pp. 2647-2653 Download
•  Zhang, S., J. Xu, L.-H. Lee, E.-P. Chew, E.-P. Wong, C.-H. Chen. 2017. Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization. IEEE Transactions on Evolutionary Computation, 21(2), 206-219. Download
•  Zhu, C., J. Xu, C.-H. Chen, L.-H. Lee, J.-Q. Hu. 2016. Balancing search and estimation in random search based stochastic simulation optimization. IEEE Transactions on Automatic Control, 61 (11), 3593-3598. Download
•  Zhang, S., L.-H. Lee, E.-P. Chew, J. Xu, C.-H. Chen. 2016. A simulation budget allocation procedure for enhancing the efficiency of optimal subset selection. IEEE Transactions on Automatic Control 61(1) 62-75. Download
•  Brantley, M.W., Lee, L.-H., Chen, C.-H., and Xu, J. 2014. An Efficient Simulation Budget Allocation Method Incorporating Regression for Partitioned Domains. Automatica, 50: 1391-1400. Download


Particle Swarm Optimization
•  Zhang, S., J. Xu, L.-H. Lee, E.-P. Chew, E.-P. Wong, C.-H. Chen. 2017. Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization. IEEE Transactions on Evolutionary Computation, 21(2), 206-219. Download
•  Taghiyeh, S., J. Xu. 2016. A new particle swarm optimization algorithm for noisy optimization. Swarm Intelligence, 10(3), 161-192. Download


Overview of simulation modeling and analysis
•  Xu, J. 2017. Model calibration. A. Tolk, J. Fowler, G.Shao, and E. Yucesan, eds. Advances In Modeling And Simulation - Seminal Research from 50 Years of Winter Simulation Conferences, Chapter 3, 27-46. Springer Series of Simulation Foundations, Methods, and Applications, New York. Download
•  Xu, J., E. Huang, C.-H. Chen, L.-H. Lee. 2015. Simulation optimization: a review and exploration in the new era of cloud computing and big data. Asia-Pacific Journal of Operational Research 32(3), 34 pages. Download
•  Hong, L. J, B. L. Nelson, J. Xu. 2014. Discrete Optimization via Simulation. In M.C. Fu (Eds.), Handbook on Simulation Optimization, Chapter 2, 9-44. Springer, New York. Download


Risk analytics and rare event simulation
•  Nagaraj, K., J. Xu, S. Ghosh, and R. Pasupathy. Efficient estimation in the tails of Gaussian copulas. arXiv:1607.01375. Download
•  Guharay, S., K.-C. Chang, J. Xu. Estimation of Value-at-Risk Using Mixture Copula Model for Heavy-Tailed Operational Risk Losses in Financial, Insurance & Climatological Data. Proceedings of 2018 International Conference on Information Fusion, 1-6, IEEE Press.
•  Guharay, S., K.-C. Chang, J. Xu. 2017. Robust estimation of Value-at-Risk through distribution-free and parametric approaches in modern operational risk management. Risks, 5(41), 30 pages. Download
•  Collamore, C., Vidyashankar, A., and Xu, J., 2013. Rare Event Simulation for Stochastic Fixed Point Equations Related to the Smoothing Transformation. In Proc. of 2013 Winter Simulation Conference, pp. 555-563, Washington, D.C. 2013.
•  Vidyashankar, A. V. and Xu, J. 2013. Adaptive Nested Rare Event Simulation Algorithms. In Proc. of 2013 Winter Simulation Conference, pp. 736-744, Washington, D.C. 2013.
•  Subramanian, D., Huang, P., Pulavarthi, C., Xu, J., Sekhar, H., Zhan, S., Tripathi, S. and Kumar, S. 2010. Risk-adjusted approach to optimize investments in product development portfolios, IBM Journal of Research & Development, 54(3). Download
•  Huang, P., Subramanian, D., Xu, J. 2010. An importance sampling method for portfolio CVaR estimation with Gaussian copula models, Winter Simulation Conference, Baltimore, Maryland.