551
Views
0
CrossRef citations to date
0
Altmetric
Operations Engineering & Analytics

Unifying offline and online simulation for online decision-making

, , &
Pages 923-935 | Received 24 May 2021, Accepted 10 Dec 2021, Published online: 01 Feb 2022

References

  • Brantley, M.W., Lee, L.H., Chen, C.-H. and Chen, A. (2013) Efficient simulation budget allocation with regression. IIE Transactions, 45(3), 291–308.
  • Chen, C.-H., Chick, S.E., Lee, L.H. and Pujowidianto, N.A. (2015) Ranking and selection: Efficient simulation budget allocation. Handbook of Simulation Optimization, Springer, New York, pp. 45–80.
  • Chen, C.-H. and Lee, L.H. (2011) Stochastic Simulation Optimization: An Optimal Computing Budget Allocation, Volume 1. World Scientific, Singapore.
  • Chen, C.-H., Lin, J., Yücesan, E. and Chick, S.E. (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dynamic Systems, 10(3), 251–270.
  • Chick, S.E. and Inoue, K. (2001) New two-stage and sequential procedures for selecting the best simulated system. Operations Research, 49(5), 732–743.
  • Ding, L., Hong, L.J., Shen, H. and Zhang, X. (2021) Technical note—Knowledge gradient for selection with covariates: Consistency and computation. Naval Research Logistics (NRL).
  • Fan, W., Hong, L.J. and Nelson, B.L. (2016) Indifference-zone-free selection of the best. Operations Research, 64(6), 1499–1514.
  • Frazier, P., Powell, W. and Dayanik, S. (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS Journal on Computing, 21(4), 599–613.
  • Frazier, P.I., Powell, W.B. and Dayanik, S. (2008) A knowledge-gradient policy for sequential information collection. SIAM Journal on Control and Optimization, 47(5), 2410–2439.
  • Gao, S., Chen, W. and Shi, L. (2017) A new budget allocation framework for the expected opportunity cost. Operations Research, 65(3), 787–803.
  • Gao, S., Du, J. and Chen, C.-H. (2019) Selecting the optimal system design under covariates, in 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), IEEE Press, Piscataway, NJ, pp. 547–552.
  • Ghiami, Y., Williams, T. and Wu, Y. (2013) A two-echelon inventory model for a deteriorating item with stock-dependent demand, partial backlogging and capacity constraints. European Journal of Operational Research, 231(3), 587–597.
  • Goldberg, P.W., Williams, C.K. and Bishop, C.M. (1997) Regression with input-dependent noise: A Gaussian process treatment. Advances in Neural Information Processing Systems, 10, 493–499.
  • Goodwin, T., Xu, J., Chen, C.-H. and Celik, N. (2021) Efficient simulation optimization with simulation learning, in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), IEEE Press, Piscataway, NJ, pp. 2268–2273.
  • He, L., Hu, Z. and Zhang, M. (2020) Robust repositioning for vehicle sharing. Manufacturing & Service Operations Management, 22(2), 241–256.
  • Hong, L.J., Fan, W. and Luo, J. (2021) Review on ranking and selection: A new perspective. Frontiers of Engineering Management, 8(3), 321–343.
  • Hong, L.J. and Jiang, G. (2019) Offline simulation online application: A new framework of simulation-based decision making. Asia-Pacific Journal of Operational Research, 36(06), 1940015.
  • Hong, L.J., Luo, J. and Nelson, B.L. (2015) Chance constrained selection of the best. INFORMS Journal on Computing, 27(2), 317–334.
  • Jiang, G., Hong, L.J. and Nelson, B.L. (2020) Online risk monitoring using offline simulation. INFORMS Journal on Computing, 32(2), 356–375.
  • Jin, X., Li, H. and Lee, L.H. (2019) Optimal budget allocation in simulation analytics, in 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), IEEE Press, Piscataway, NJ, pp. 178–182.
  • Jones, D.R., Schonlau, M. and Welch, W.J. (1998) Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4), 455–492.
  • Lázaro-Gredilla, M. and Titsias, M.K. (2011) Variational heteroscedastic Gaussian process regression, in Proceedings of the 28th International Conference on Machine Learning (ICML), Bellevue, WA, USA.
  • Li, H., Lam, H., Liang, Z. and Peng, Y. (2020) Context-dependent ranking and selection under a Bayesian framework. arXiv:2012.05577.
  • Lin, Y., Nelson, B.L. and Pei, L. (2019) Virtual statistics in simulation via k nearest neighbors. INFORMS Journal on Computing, 31(3), 576–592.
  • Liu, H., Ong, Y.-S., Shen, X. and Cai, J. (2020) When Gaussian process meets big data: A review of scalable GPs. IEEE Transactions on Neural Networks and Learning Systems, 31(11), 4405–4423.
  • Liu, H., Xiao, H., Li, H., Lee, L.H. and Chew, E.P. (2021) Offline sequential learning via simulation. IISE Transactions, 1–30.
  • Long, Y., Lee, L.H. and Chew, E.P. (2012) The sample average approximation method for empty container repositioning with uncertainties. European Journal of Operational Research, 222(1), 65–75.
  • Lu, T., Lee, C.-Y. and Lee, L.-H. (2020) Coordinating pricing and empty container repositioning in two-depot shipping systems. Transportation Science, 54(6), 1697–1713.
  • Nelson, B.L. (2016) ‘Some tactical problems in digital simulation’ for the next 10 years. Journal of Simulation, 10(1), 2–11.
  • Ouyang, H. and Nelson, B.L. (2017) Simulation-based predictive analytics for dynamic queueing systems, in 2017 Winter Simulation Conference (WSC), IEEE Press, Piscataway, NJ, pp. 1716–1727.
  • Pedrielli, G., Selcuk Candan, K., Chen, X., Mathesen, L., Inanalouganji, A., Xu, J., Chen, C.-H. and 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.
  • Peng, Y., Chong, E.K., Chen, C.-H. and Fu, M.C. (2018) Ranking and selection as stochastic control. IEEE Transactions on Automatic Control, 63(8), 2359–2373.
  • Rasmussen, C. and Williams, C. (2006) Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA.
  • Scott, W., Frazier, P. and Powell, W. (2011) The correlated knowledge gradient for simulation optimization of continuous parameters using Gaussian process regression. SIAM Journal on Optimization, 21(3), 996–1026.
  • Shen, H., Hong, L.J. and Zhang, X. (2021) Ranking and selection with covariates for personalized decision making. INFORMS Journal on Computing, 33(4), 1259–1684.
  • Xiao, H., Lee, L.H. and Chen, C. (2015 July) Optimal budget allocation rule for simulation optimization using quadratic regression in partitioned domains. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(7), 1047–1062.
  • Xiao, H., Lee, L.H., Morrice, D., Chen, C.-H. and Hu, X. (2021) Ranking and selection for terminating simulation under sequential sampling. IISE Transactions, 53(7), 735–750.
  • Xu, J., Zhang, S., Huang, E., Chen, C.-H., Lee, L.H. and Celik, N. (2016) MO2TOS: Multi-fidelity optimization with ordinal transformation and optimal sampling. Asia-Pacific Journal of Operational Research, 33(3), 1650017.
  • Zhong, Y., Liu, S., Luo, J. and Hong, L.J. (2021) Speeding up Paulson’s procedure for large-scale problems using parallel computing. INFORMS Journal on Computing.

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.