Publications

Li, W., Z. Sun, and L. J. Hong (2022). Who is next: Patient prioritization under emergency department blocking. Operations Research, forthcoming.

Project Preprint

Zhang, X., H. Shen, L. J. Hong, and L. Ding (2022). Technical Note: Knowledge gradient for selection with covariates: consistency and computation. Naval Research Logistics, forthcoming.

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Zhong, Y., J. Luo, S. Liu, and L. J. Hong (2022). Speeding up Paulson's Procedure for large-scale problems using parallel computing. INFORMS Journal on Computing, forthcoming.

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Peng, Y., L. Xiao, B. Heidergott, L. J. Hong, and H. Lam (2022). A new likelihood ratio method for training artificial neural networks. INFORMS Journal on Computing, forthcoming.

Project Project Preprint

Hong, L. J., G. Liu, J. Luo, and J. Xie (2022). Variability scaling and capacity planning in Covid-19 pandemic. Working paper.

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Hong, L. J., G. Jiang, and Y. Zhong (2022). Solving large-scale fixed-budget ranking and selection problems. INFORMS Journal on Computing, submitted.

Project Preprint

Hu, Z. and L. J. Hong (2022). Robust simulation with likelihood-ratio constrained input uncertainty. INFORMS Journal on Computing, forthcoming.

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Jiang, X., B. L. Nelson, and L. J. Hong (2022). Meaningful sensitivities: A new family of simulation sensitivity measures. IISE Transactions, 54:122-133.

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Wang, T. and L. J. Hong (2022). Large-scale inventory optimization: A recurrent-neural-networks-inspired simulation approach. INFORMS Journal on Computing, submitted.

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Zhong, Y. and L. J. Hong (2022). Knockout-tournament procedures for large-scale ranking and selection in parallel computing environments. Operations Research, 70:432-453.

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Zhang, Y., J. Zhang, J. Zhang, J. Wang, K. Lu and L. J. Hong (2022). Integrating Algorithmic Sampling-Based Motion Planning with Learning in Autonomous Driving. ACM Transactions on Intelligent Systems and Technology, 13(3):1-27.

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Wang, X., L. J. Hong, Z. Jiang, and H. Shen (2022). Gaussian process based search for continuous optimization via simulation. Operations Research, submitted.

Project Preprint

Li, W., N. Chen, and L. J. Hong (2022). Dimension reduction in contextual online learning via nonparametric variable selection. Journal of Machine Learning Research, submitted.

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Hong, L. J. and X. Zhang (2021). Surrogate-based simulation optimization. INFORMS TutORials in Operations Research, 287-311.

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Hong, L. J., W. Fan, and J. Luo (2021). Review on ranking and selection: A new perspective. Frontier of Engineering Management, 8:321-343.

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Shen, H., L. J. Hong, and X. Zhang (2021). Ranking and selection with covariates for personalized decision making. INFORMS Journal on Computing, 33:1500-1519.

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Wang, S. and L. J. Hong (2021). Option Pricing By Neural Stochastic Differential Equations: A Simulation-optimization Approach. Proceedings of the 2021 Winter Simulation Conference, forthcoming.

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Hong, L. J., Z. Huang, and H. Lam (2021). Learning-based robust optimization procedures and statistical guarantee. Management Science, 67:3321-3984.

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Zhong, Y., L. J. Hong, and G. Liu (2021). Earning and learning with varying cost. Production and Operations Management, 30:2379-2394.

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Xiao, L., Y. Peng, L. J. Hong, Z. Ke and S. Yang (2020). Training Artificial Neural Networks by Generalized Likelihood Ratio Method: An Effective Way to Improve Robustness. IEEE 16th International Conference on Automation Science and Engineering (CASE), pp.1343-1348.

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Hong, L. J., C. Li, and J. Luo (2020). Technical note: Finite‐time regret analysis of Kiefer‐Wolfowitz stochastic approximation algorithm and nonparametric multi‐product dynamic pricing with unknown demand. Naval Research Logistics, 67:368-379.

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Jiang, G., L. J. Hong, and B. L. Nelson (2020). Online risk monitoring using offline simulation. INFORMS Journal on Computing, 32:356-375.

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Fan, W., L. J. Hong, and X. Zhang (2020). Distributionally robust selection of the best. Management Science, 66:190-208.

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Zhang, Y., J. Zhang, J. Zhang, J. Wang, K. Lu and L. J. Hong (2020). A novel learning framework for sampling-based motion planning in autonomous driving. Proceedings of the AAAI Conference on Artificial Intelligence, pp.1202-1209.

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Yun, X., L. J. Hong, G. Jiang, and S. Wang (2019). On gamma estimation via matrix kriging. Naval Research Logistics, 66:393-410.

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Hong, L. J. and G. Jiang (2019). Offline simulation online application: A new framework of simulation-based decision making. Asia-Pacific Journal of Operational Research, 36:1940015.

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Jiang, W. X., B. L. Nelson, and L. J. Hong (2019). Estimating sensitivity to input model variance. Proceedings of the 2019 Winter Simulation Conference, pp.3705-3716.

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Sun, W., Z. Hu, and L. J. Hong (2018). Gaussian Mixture Model-based Random Search for Continuous Optimization via Simulation. Proceedings of the 2018 Winter Simulation Conference, pp.2003-2014.

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Zhong, Y. and L. J. Hong (2018). Fully sequential ranking-and-selection procedures with PAC guarantee. Proceedings of the 2018 Winter Simulation Conference, pp.1898-1908.

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Shen, H., L. J. Hong, and X. Zhang (2018). Enhancing stochastic kriging for queueing simulation with stylized models. IISE Transactions, 50:943–958.

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Fang, J. and L. J. Hong (2018). A simulation-based estimation method for bias reduction. IISE Transactions, 50:14-26.

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Shen, H., L. J. Hong, and X. Zhang (2017). Ranking and selection with covariates. Proceedings of the 2017 Winter Simulation Conference, pp.2137-2148.

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Hong, L. J., S. Juneja, and G. Liu (2017). Kernel smoothing for nested estimation with application to portfolio risk measurement. Operations Research, 65:657-673.

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Hong, L. J. and G. Jiang (2017). Gradient and Hessian of joint probability functions with applications on chance constrained programs. Journal of Operations Research Society of China, 5:431-455.

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Zhong, Y. and L. J. Hong (2017). A new framework of designing sequential ranking-and-selection procedures. Proceedings of the 2017 Winter Simulation Conference, pp.2237-2244.

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Hong, L. J., J. Luo, and Y. Zhong (2016). Speeding up pairwise comparisons for large scale ranking and selection. Proceedings of the 2016 Winter Simulation Conference, pp.749-757.

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Fan, W., L. J. Hong, and B. L. Nelson (2016). Indifference-zone-free selection of the best. Operations Research, 64:1499-1514.

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Hong, L. J., Z. Huang, and H. Lam (2016). Approximating data-driven joint chance constrained programs via uncertainty set construction. Proceedings of the 2016 Winter Simulation Conference, pp.389-400.

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Jiang, G., L. J. Hong, and B. L. Nelson (2016). A simulation analytics approach to dynamic risk monitoring. Proceedings of the 2016 Winter Simulation Conference, pp.437-447.

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Hu, Z. and L. J. Hong (2015). Robust simulation of stochastic systems with input uncertainties modeled by statistical divergences. Proceedings of the 2015 Winter Simulation Conference, pp.643-654.

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Song, E., B. L. Nelson, and L. J. Hong (2015). Input uncertainty and indifference-zone ranking and selection. Proceedings of the 2015 Winter Simulation Conference, pp.414-424.

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Luo, J., L. J. Hong, B. L. Nelson, and Y. Wu (2015). Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments. Operations Research, 63:1177-1194.

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Hong, L. J., J. Luo, and B. L. Nelson (2015). Chance constrained selection of the best. INFORMS Journal on Computing, 27:317-334.

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Hong, L. J., X. Xu, and S. H. Zhang (2015). Capacity reservation for time-sensitive service providers: An application in seaport management. European Journal of Operational Research, 245:470-479.

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Hong, L. J. and H. Lam (2015). A statistical perspective on linear programs with uncertain parameters. Proceedings of the 2015 Winter Simulation Conference, pp.3690-3701.

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Zhang, X., L. J. Hong, and J. Zhang (2014). Scaling and modeling of call center arrivals. Proceedings of the 2014 Winter Simulation Conference, pp.476-485.

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Hong, L. J. Z. Hu, and G. Liu (2014). Monte Carlo methods for value-at-risk and conditional value-at-risk: A review. ACM Transactions on Modeling and Computer Simulation, 24:22/1-22/37.

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Hong, L. J., S. Juneja, and J. Luo (2014). Estimating sensitivities of portfolio credit risk using Monte Carlo. INFORMS Journal on Computing, 26:848-856.

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Hong, L. J., Z. Hu, and L. Zhang (2014). Conditional value-at-risk approximation to value-at-risk constrained programs: A remedy via Monte Carlo. INFORMS Journal on Computing, 26:385-400.

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Sun, L., L. J. Hong and Z. Hu (2014). Balancing exploitation and exploration in discrete optimization via simulation through a Gaussian process-based search. Operations Research, 62:1416-1438.

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Fan, W. and L. J. Hong (2014). A frequentist selection-of-the-best procedure without indifference zone. Proceedings of the 2014 Winter Simulation Conference, pp.3737-3748.

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Chang, K. H., L. J. Hong, and H. Wan (2013). Stochastic trust-region response-surface method (STRONG) – A new response surface framework for simulation optimization. INFORMS Journal on Computing, 25:230-243.

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Fan, W., L. J. Hong, and X. Zhang (2013). Robust selection of the best. Proceedings of the 2013 Winter Simulation Conference, pp.868-876.

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Fang, J. and L. J. Hong (2013). Linking estimation and performance evaluation through simulation. Proceedings of the 2013 Winter Simulation Conference, pp.766-777.

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Xu, J., B. L. Nelson and L. J. Hong (2013). An adaptive hyperbox algorithm for high-dimensional discrete optimization via simulation problems. INFORMS Journal on Computing, 25:133-146.

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Hu, Z., L. J. Hong, and A. M.-C. So (2013). Ambiguous Probabilistic Programs. Technical report.

PDF Project Preprint

Hu, Z., L. J. Hong, and L. Zhang (2013). A smooth Monte Carlo approximation to joint chance-constrained programs. IIE Transactions, 45:716-735.

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Hu, Z., Cao, J., and L. J. Hong (2012). Robust simulation of global warming policies using the DICE model. Management Science, 58:2190-2206.

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Hu, Z. and L. J. Hong (2012). Kullback-Leibler divergence constrained distributionally robust optimization. Technical report.

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Zhang, J., L. J. Hong, and R. Q. Zhang (2012). Fighting strategies in a market with counterfeits. Annals of Operations Research, 192:49-66.

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Hong, L. J., Y. Yang, and L. Zhang (2011). Sequential convex approximations to joint chance constrained programs: A Monte Carlo approach. Operations Research, 59:617-630.

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Sun, L., L. J. Hong and Z. Hu (2011). Optimization via simulation using Gaussian process-based search. Proceedings of the 2011 Winter Simulation Conference, pp.4134-4145.

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Hong, L. J. and G. Liu (2011). Monte Carlo estimation of value-at-risk, conditional value-at-risk and their sensitivities. Proceedings of the 2011 Winter Simulation Conference, pp.95-107 (invited advanced tutorial talk).

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Luo, J. and L. J. Hong (2011). Large-scale ranking and selection using cloud computing. Proceedings of the 2011 Winter Simulation Conference, pp.4051-4061.

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Liu, G. and L. J. Hong (2011). Kernel estimation of the Greeks of financial options. Operations Research, 59:96-108.

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Hong, L. J., B. L. Nelson, and J. Xu (2010). Speeding up COMPASS for high-dimensional discrete optimization via simulation. Operations Research Letters, 38:550-555.

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Hu, Z., J. Cao, and L. J. Hong (2010). Robust simulation of environmental policies. Proceedings of the 2010 Winter Simulation Conference, pp.295-1305.

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Hong, L. J. and G. Liu (2010). Pathwise estimation of probability sensitivities through terminating and steady-state simulations. Operations Research, 58:357-370.

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Xu, J., B. L. Nelson and L. J. Hong (2010). Industrial Strength COMPASS: A comprehensive algorithm and software for optimization via simulation. ACM Transactions on Modeling and Computer Simulation, 20: 3/1-3/29.

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Sun, L. and L. J. Hong (2010). Asymptotic representations for importance-sampling estimators of value-at-risk and conditional value-at-risk. Operations Research Letters, 38:246-251.

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Sun, L. and L. J. Hong (2009). The asymptotic expansions of value-at-risk and conditional value-at-risk. Proceedings of the 2009 Winter Simulation Conference, pp.15-422.

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Hong, L. J. and G. Liu (2009). Simulating sensitivities of conditional value-at-risk. Management Science, 55: 281-293.

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Liu, G. and L. J. Hong (2009). Revisit of stochastic mesh method for pricing American options. Operations Research Letters, 37:411-414.

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Liu, G and L. J. Hong (2009). Kernel estimation of quantile sensitivities. Naval Research Logistics, 56: 511-525.

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Hong, L. J (2009). Estimating quantile sensitivities. Operations Research, 57: 118-130.

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Hong, L. J. and S. Juneja (2009). Estimating expectations of nonlinear functions. Proceedings of the 2009 Winter Simulation Conference, pp.223-1236.

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Fu, M. C., L. J. Hong and J. Q. Hu (2009). Conditional Monte Carlo estimation of quantile sensitivities. Management Science, 55: 2019-2027.

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Hong, L. J. and B. L. Nelson (2009). A brief introduction to optimization via simulation. Proceedings of the 2009 Winter Simulation Conference, pp.75-85. (invited introductory tutorial talk).

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Liu, G. and L. J. Hong (2008). Revisit of stochastic mesh method for pricing American options. Proceedings of the 2008 Winter Simulation Conference, pp.94-601.

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Chang, K. H., L. J. Hong, and H. Wan (2007). Stochastic trust region gradient-free method: A new response-surface-based algorithm for simulation optimization. Proceedings of the 2007 Winter Simulation Conference, pp.346-354.

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Hong, L. J. and B. L. Nelson (2007). Selecting the best system when systems are revealed sequentially. IIE Transactions, 39:723-734.

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Chen, N. and L. J. Hong (2007). Monte-Carlo method in financial engineering. Proceedings of the 2007 Winter Simulation Conference, pp.919-931 (invited advanced tutorial talk).

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Liu, G. and L. J. Hong (2007). Kernel estimation of quantile sensitivity. Proceedings of the 2007 Winter Simulation Conference, pp.941-948.

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Hong, L. J. and B. L. Nelson (2007). A framework of locally convergent random search algorithms for discrete optimization via simulation. ACM Transactions on Modeling and Computer Simulation, 17: 19/1-19/22.

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Hong, L. J (2006). Fully sequential indifference-zone selection procedures with variance dependent sampling. Naval Research Logistics, 53:464-476.

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Hong, L. J. and B. L. Nelson (2006). Discrete optimization via simulation using COMPASS. Operations Research, 54:115-129.

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Wang, K. L., N. M. Tsang, H. W. Tsui, W. Y. Sze, L. J. Hong, and C. Y. Lee (2006). Analyzing capacity competition among the airports in the Pearl River Delta. 2006 Annual Conference of Institute of Industrial Engineers (Hong Kong).

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Pichitlamken, J., B. L. Nelson, and L. J. Hong (2006). A sequential procedure for neighborhood selection-of-the-best in optimization via simulation. European Journal of Operational Research, 173:283-298.

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Hong, L. J. and B. L. Nelson (2005). The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection. IIE Transactions, 37:623-634.

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Hong, L. J (2005). Discrete optimization via simulation using coordinate search. Proceedings of the 2005 Winter Simulation Conference, pp.803-810.

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Hong, L. J. and B. L. Nelson (2003). An Indifference-zone Selection Procedure With Minimum Switching and Sequential Sampling. Proceedings of the 2003 Winter Simulation Conference, pp.474-480.

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Hong, L. J. and B. L. Nelson (2001). Robust evaluation of flatness and straightness tolerance using simulated annealing. Transactions of NAMRI/SME, 29:553-560.

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