참고문헌
- Andrad'ottir, S. and S.-H. Kim (2010) "Fully sequential procedures for comparing constrained systems via simulation", Naval Research Logistics, 57(5), 403-421. https://doi.org/10.1002/nav.20408
- Batur, D. and S.-H, Kim (2005) "Procedures for feasibility detection in the presence of multiple constraints", Proceedings of the 2005 Winter Simulation Conference, Orlando, FL, USA.
- Beirlant, J., E. J. Dudewicz, and E. C. van der Meulen (1982) "Complete statistical ranking of populations with tables and applications", Journal of Computational and Applied Mathematics, 8(3), 187-201. https://doi.org/10.1016/0771-050X(82)90041-9
- Branke, J., S. E. Chick, and C. Schmidt (2007) "Selecting a selection procedure", Management Science, 53(12), 1916-1932. https://doi.org/10.1287/mnsc.1070.0721
- Butler, J., D. J. Morrice, and P. W. Mullarkey (2001) "A multiple attribute utility theory approach to ranking and selection", Management Science, 47(6), 800-816. https://doi.org/10.1287/mnsc.47.6.800.9812
- Chen, C.-H. and L. H. Lee (2011) Stochastic Simulation Optimization: An Optimal Computing Budget Allocation, World Scientific, Singapore.
- Chen, C.-H., J. Lin, E. Yücesan, and S. E. Chick (2000) "Simulation budget allocation for further enhancing the efficiency of ordinal optimization", Discrete Event Dynamin Systems: Theory and Applications, 10(3), 251-270. https://doi.org/10.1023/A:1008349927281
- Chen, C.-H., D. He, M. Fu, and L. H. Lee (2008) "Efficient simulation budget allocation for selecting an optimal subset", INFORMS Journal on Computing, 20(4), 579-595. https://doi.org/10.1287/ijoc.1080.0268
- Chick, S. E. and K. Inoue (2001) "New two-stage and sequential procedures for selecting the best simulated system", Operations Research, 49(5), 732-743. https://doi.org/10.1287/opre.49.5.732.10615
- Choi, S. H. and C.-B. Choi (2020) "A simulation budget allocation procedure for finding both extreme designs simultaneously in discrete-event simulatio", IEEE Access, 8, 93978-93986. https://doi.org/10.1109/access.2020.2995196
- Choi, S. H. and T. G. Kim (2018a) "Optimal subset selection of stochastic model using statistical hypothesis test", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(4), 557-564. https://doi.org/10.1109/tsmc.2016.2608982
- Choi, S. H. and T. G. Kim (2018b) "Pareto set selection for multiobjective stochastic simulation model", IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2018.2846680.
- Choi, S. H. and T. G. Kim (2018c) "Efficient ranking and selection for stochastic simulation model based on hypothesis test", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(9), 1555-1565. https://doi.org/10.1109/tsmc.2017.2679192
- Choi, S. H. and T. G. Kim (2018d) "A heuristic approach for selecting best-subset including ranking within the subset", IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2018.2870408.
- Choi, S. H., K.-M. Seo, and T. G. Kim (2017) "Accelerated simulation of discrete event dynamic systems via a multi-fidelity modeling framework", Applied Sciences, 7(10):1056. https://doi.org/10.3390/app7101056
- Choi, S. H., B. G. Kang, and T. G. Kim (2019a) "A heuristic procedure for selecting the feasible best design in the presence of stochastic constraints", IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2019.2894523.
- Choi, S. H., C.-B. Choi, and T. G. Kim (2019b) "An improved budget allocation procedure for selecting the best-simulated design in the presence of large stochastic noise", IEEE Access, 7, 154435-154446. https://doi.org/10.1109/access.2019.2948980
- Eberhart, R. and J. Kennedy (1995) "A new optimizer using particle swarm theory", Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS '95), Nagoya, Japan, 39-43.
- Frazier, P. I. and A. M. Kazachkov (2011) "Guessing preferences: a new approach to multi-attribute ranking and selection", Proceedings of the 2011 Winter Simulation Conference, Phoenix, AZ, USA, 4319-4331.
- Gao S. and W. Chen (2015) "Efficient subset selection for the expected opportunity cost", Automatica, 59, 19-26. https://doi.org/10.1016/j.automatica.2015.06.005
- Holland, J. H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, USA.
- Hong, J. H. and T. G. Kim (2010) "Interoperation between engineering- and engagement-level models for system effectiveness analysis", Journal of the Korea Society for Simulation, 19(4), 319-326.
- Hong, J. H., K.-M. Seo, and T. G. Kim (2015) "Reverse simulation software architecture for required performance analysis of defense system", The Journal of Korean Institute of Communications and Information Sciences, 40(4), 750-759. https://doi.org/10.7840/kics.2015.40.4.750
- IEEE Computer Society (2010) IEEE standard for modeling and simulation (M&S) High level architecture (HLA) - framework and rules, IEEE Standard 1516-2010.
- Jia, Q. S. (2012) "Efficient computing budget allocation for simulation-based policy improvement", IEEE Transactions on Automation Science and Engineering, 9(2), 342-352. https://doi.org/10.1109/TASE.2011.2181164
- Kang, B. G. and T. G. Kim (2018) "Method for analysis of C3 systems of systems using transformation of federation based on an extended DEVS formalism", Journal of the Korea Society for Simulation, 27(3), 13-21. https://doi.org/10.9709/JKSS.2018.27.3.013
- Kang, B. G., K.-M. Seo, and T. G. Kim (2019) "Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems", Simulation: Transactions of the Society for Modeling and Simulation International, 95(8), 673-691. https://doi.org/10.1177/0037549718809890
- Kim, T. G. (2007) "Modeling and simulation engineering", Communication of the Korea Information Science Society, 25(11), 5-15.
- Kim, T. G. (2015) "Development of reverse simulation engine for extraction of engineering specification and tactics of weapon systems from given effectiveness", KAIST.
- Kim, T. G. (2018a) Defense Modeling and Simulation, Hanteemedia, Seoul, South Korea.
- Kim, T.-Y. (2018b) "Simulation reconfiguration using entity plug-in approach for weapon system effectiveness anaylsis", Journal of the Korea Society for Simulation, 27(2), 49-59. https://doi.org/10.9709/JKSS.2018.27.2.049
- Kim, S.-H. and B. L. Nelson (2001) "A fully sequential procedure for indifference-zone selection in simulation", ACM Transactions on Modeling and Computer Simulation, 11(3), 251-273. https://doi.org/10.1145/502109.502111
- Kirkpatrick, S., C. D. Gelatt, and M. P. Vecchi (1983) "Optimization by simulated annealing", Science, 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671
- Lee, L. H., E. P. Chew, S. Teng, and D. Goldsman (2010a) "Finding the nondominated Pareto set for multi-objective simulation models", IIE Transactions, 42(9), 656-674. https://doi.org/10.1080/07408171003705367
- Lee L. H., E. P. Chew, and S. Teng (2010b) "Computing budget allocation rules for multi-objective simulation models based on different measures of selection quality", Automatica, 46(12), 1935-1950. https://doi.org/10.1016/j.automatica.2010.08.004
- Lee, L. H., N. A. Pujowidianto, L.-W. Li, C.-H. Chen, and C. M. Yap (2012) "Approximate simulation budget allocation for selecting the best design in the presence of stochastic constraints", IEEE Transactions on Automatic Control, 57(11), 2940-2945. https://doi.org/10.1109/TAC.2012.2195931
- Lee, W.-B., J.-H. Kim, and T. G. Kim (2006) "War game simulation using parametirc behavior modeling method", Journal of the Korea Contents Association, 6(11), 126-134.
- Li, J., W. Liu, G. Pedrielli, L. H. Lee, and E. P. Chew (2018) "Optimal computing budget allocation to select the nondominated systems A large deviations perspective", IEEE Transactions on Automatic Control, 63(9), 2913-2927. https://doi.org/10.1109/tac.2017.2779603
- Seo, K.-M., H. S. Song, S. J. Kwon, and T. G. Kim (2011) "Measurement of effectiveness for an antitorpedo combat system using a discrete event systems specification-based underwater warfare simulator", The Journal of Defense Modeling and Simulation, 8(3), 157-171. https://doi.org/10.1177/1548512910390245
- Shortle, J. F., C. H. Chen, B. Crain, A. Brodsky, and D. Brod (2012) "Optimal splitting for rare-event simulation", IIE Transactions, 44(5), 352-367. https://doi.org/10.1080/0740817X.2011.596507
- Teng, S., L. H. Lee, and E. P. Chew (2010) "Integration of indifference-zone with multi-objective computing budget allocation", European Journal of Operational Research, 203(2), 419-429. https://doi.org/10.1016/j.ejor.2009.08.008
- Wang, Y., L. Luangkesorn, and L. J. Shuman (2011) "Best-subset selection procedure", Proceedings of the 2011 Winter Simulation Conference, Phoenix, AZ, USA, 4310-4318.
- Xiao, H. and S. Gao (2018) "Simulation budget allocation for selecting the top-m designs with input uncertainty", IEEE Transactions on Automatic Control, 63(9), 3127-3134. https://doi.org/10.1109/tac.2018.2791425
- Xiao, H. and L. H. Lee (2014) "Simulation optimization using genetic algorithms with optimal computing budget allocation", Simulation: Transactions of the Society for Modeling and Simulation International, 90(10), 1146-1157. https://doi.org/10.1177/0037549714548095
- Xiao, H., L. H. Lee, and K. M. Ng (2014) "Optimal computing budget allocation for complete ranking", IEEE Transactions on Automation Science and Engineering, 11(2), 516-524. https://doi.org/10.1109/TASE.2013.2239289
- Xiao, H., S. Gao, and L. H. Lee (2017) "Simulation budget allocation for simultaneously selecting the best and worst subsets", Automatica, 84, 117-127. https://doi.org/10.1016/j.automatica.2017.07.006
- Xiao, H., H. Chen, and L. H. Lee (2019) "An efficient simulation procedure for ranking the top simulated designs in the presence of stochastic constraints", Automatica, 103, 106-115. https://doi.org/10.1016/j.automatica.2018.12.008
- Xiao, H., F. Gao, and L. H. Lee (2019b) "Optimal computing budget allocation for complete ranking with input uncertainty", IISE Transactions, 52(5), 489-499. https://doi.org/10.1080/24725854.2019.1659524
- Xu, J., E. Huang, C.-H. Chen, and 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):1550019. https://doi.org/10.1142/S0217595915500190
- Xu, J., E. Huang, L. Hsieh, L.-H. Lee, and C.-H. Chen (2016) "Simulation optimization in the era of industrial 4.0 and industrial Internet", Journal of Simulation, 10(4), 310-320. https://doi.org/10.1057/s41273-016-0037-6
- Zhang, S., L. H. Lee, E. P. Chew, J. Xu, and C.-H. Chen (2016a) "A simulation budget allocation procedure for enhancing the efficiency of optimal subset selection", IEEE Transactions on Automatic Control, 61(1), 62-75. https://doi.org/10.1109/TAC.2015.2423832
- Zhang, J., C. Wang, D. Zang, and M. Zhou (2016b) "Incorporation of optimal computing budget allocation for ordinal optimization into learning automata", IEEE Transactions on Automation Science and Engineering, 13(2), 1008-1017. https://doi.org/10.1109/TASE.2015.2450535
- Zhang, J., Z. Li, C.Wang, D. Zang, and M. Zhou (2017a) "Approximate simulation budget allocation for subset ranking", IEEE Transactions on Control Systems Technology, 25(1), 358-365. https://doi.org/10.1109/TCST.2016.2539329
- Zhang, J., L. Zhang, C. Wang, and M. Zhou (2017b) "Approximately optimal computing budget allocation for selection of the best and worst designs", IEEE Transactions on Automtic Control, 62(7), 3249-3261. https://doi.org/10.1109/TAC.2016.2628158