Optimal computing budget allocation for selecting the optimal subset of multi-objective simulation optimization problems
Authors: 
Hui Xiao, Minhao Cao, Lu Zhen, Xiaofan WangAuthors Info & Claims
References
[1]
Batur D., Choobineh F.F., Selecting the best alternative based on its quantile, INFORMS Journal on Computing 33 (2) (2021) 657–671.
[2]
Batur D., Kim S.H., Finding feasible systems in the presence of constraints on multiple performance measures, ACM Transactions on Modeling and Computer Simulation 20 (3) (2010) 1–26.
[3]
Bosman P.A., Thierens D., Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms, International Journal of Approximate Reasoning 31 (3) (2002) 259–289.
[4]
Bosman, P. A., & Thierens, D. (2006). Multi-objective optimization with the naive MIDEA. In Towards a new evolutionary computation: Advances in the estimation of distribution algorithms (pp. 123–157).
[5]
Boyd S., Vandenberghe L., Convex optimization, Cambridge University Press, New York, 2004.
[6]
Branke J., Chick S.E., Schmidt C., Selecting a selection procedure, Management Science 53 (12) (2007) 1916–1932.
[7]
Butler J., Morrice D.J., Mullarkey P.W., A multiple attribute utility theory approach to ranking and selection, Management Science 47 (6) (2001) 800–816.
[8]
Cao M., Kou G., Xiao H., Lee L.H., An efficient simulation procedure for the expected opportunity cost using metamodels, Automatica 157 (2023).
[9]
Chen W., Gao S., Chen W., Du J., Optimizing resource allocation in service systems via simulation: A Bayesian formulation, Production and Operations Management 32 (1) (2023) 65–81.
[10]
Chen C.H., Lee L.H., Stochastic simulation optimization: An optimal computing budget allocation, World scientific, Singapore, 2010.
[11]
Chen C.H., Lin J., Yücesan E., Chick S.E., Simulation budget allocation for further enhancing the efficiency of ordinal optimization, Discrete Event Dynamic Systems: Theory and Applications 10 (3) (2000) 251–270.
[12]
Dembo A., Zeitouni O., Large deviations techniques and applications, 2nd ed., Springer, New York, 1998.
[13]
Du D.Z., Pardalos P.M., Minimax and applications, Kluwer, Dordrecht, 1995.
[14]
Erickson, M., Mayer, A., & Horn, J. (2001). The niched pareto genetic algorithm 2 applied to the design of groundwater remediation systems. In International conference on evolutionary multi-criterion optimization (pp. 681–695).
[15]
Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Proceedings of the 5th international conference on genetic algorithms (pp. 416–423).
[16]
Fu Y., Xiao H., Lee L.H., Huang M., Stochastic optimization using grey wolf optimization with optimal computing budget allocation, Applied Soft Computing 103 (2021).
[17]
Gao S., Chen W., Efficient feasibility determination with multiple performance measure constraints, IEEE Transactions on Automatic Control 62 (1) (2017) 113–122.
[18]
Gao S., Chen W., Shi L., A new budget allocation framework for the expected opportunity cost, Operations Research 65 (3) (2017) 787–803.
[19]
Glynn, P., & Juneja, S. (2004). A large deviations perspective on ordinal optimization. In Proceedings of the 2004 winter simulation conference (pp. 577–585).
[20]
Groves M., Branke J., Top-κ selection with pairwise comparisons, European Journal of Operational Research 274 (2) (2019) 615–626.
[21]
He D., Li H., Du H., Lexicographic multi-objective MPC for constrained nonlinear systems with changing objective prioritization, Automatica 125 (2021).
[22]
Khandelwal D., Schoukens M., Tóth R., Automated multi-objective system identification using grammar-based genetic programming, Automatica 154 (2023).
[23]
Kim S.H., Nelson B.L., A fully sequential procedure for indifference-zone selection in simulation, ACM Transactions on Modeling and Computer Simulation 11 (3) (2001) 251–273.
[24]
Kou G., Xiao H., Cao M., Lee L.H., Optimal computing budget allocation for the vector evaluated genetic algorithm in multi-objective simulation optimization, Automatica 129 (2021).
[25]
Lee L.H., Chew E.P., Teng S., Computing budget allocation rules for multi-objective simulation models based on different measures of selection quality, Automatica 46 (12) (2010) 1935–1950.
[26]
Lee L.H., Chew E.P., Teng S., Goldsman D., Finding the non-dominated Pareto set for multi-objective simulation models, IIE Transactions 42 (9) (2010) 656–674.
[27]
Li J., Optimal computing budget allocation for multi-objective simulation optimization, (Ph.D. dissertation) National University of Singapore, 2012.
[28]
Li J., Liu W., Pedrielli G., Lee L.H., Chew E.P., Optimal computing budget allocation to select the nondominated systems—A large deviations perspective, IEEE Transactions on Automatic Control 63 (9) (2018) 2913–2927.
[29]
Lv L., Shen W., An improved NSGA-II with local search for multi-objective integrated production and inventory scheduling problem, Journal of Manufacturing Systems 68 (2023) 99–116.
[30]
Peng Y., Xu J., Lee L.H., Hu J., Chen C.H., Efficient simulation sampling allocation using multifidelity models, IEEE Transactions on Automatic Control 64 (8) (2018) 3156–3169.
[31]
Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of the first international conference on genetic algorithms, genetic algorithms and their applications (pp. 93–100).
[32]
Shi Z., Gao S., Xiao H., Chen W., A worst-case formulation for constrained ranking and selection with input uncertainty, Naval Research Logistics 66 (8) (2019) 648–662.
[33]
Shi Z., Peng Y., Shi L., Chen C.H., Fu M.C., Dynamic sampling allocation under finite simulation budget for feasibility determination, INFORMS Journal on Computing 34 (1) (2022) 557–568.
[34]
Szechtman, R., & Yucesan, E. (2008). A new perspective on feasibility determination. In 2008 winter simulation conference (pp. 273–280).
[35]
Szechtman, R., & Yücesan, E. (2016). A Bayesian approach to feasibility determination. In 2016 winter simulation conference (pp. 782–790).
[36]
Teng S., Lee L.H., Chew E.P., Integration of indifference-zone with multi-objective computing budget allocation, European Journal of Operational Research 203 (2) (2010) 419–429.
[37]
Wang W., Wan H., Chen X., Bonferroni-free and indifference-zone-flexible sequential elimination procedures for ranking and selection, Operations Research (2023),.
[38]
Xiao H., Chen H., Lee L.H., An efficient simulation procedure for ranking the top simulated designs in the presence of stochastic constraints, Automatica 103 (2019) 106–115.
[39]
Xiao H., Gao S., Simulation budget allocation for selecting the top-m designs with input uncertainty, IEEE Transactions on Automatic Control 63 (9) (2018) 3127–3134.
[40]
Xiao H., Gao F., Lee L.H., Optimal computing budget allocation for complete ranking with input uncertainty, IISE Transactions 52 (5) (2020) 489–499.
[41]
Xiao H., Zhang Y., Kou G., Zhang S., Branke J., Ranking and selection for pairwise comparison, Naval Research Logistics 70 (3) (2023) 284–302.
[42]
Xie J., Frazier P.I., Sequential Bayes-optimal policies for multiple comparisons with a known standard, Operations Research 61 (5) (2013) 1174–1189.
[43]
Xu J., Huang E., Chen C.H., Lee L.H., Simulation optimization: A review and exploration in the new era of cloud computing and big data, Asia-Pacific Journal of Operational Research 32 (3) (2015).
[44]
Zhang F., Song J., Dai Y., Xu J., Semiconductor wafer fabrication production planning using multi-fidelity simulation optimisation, International Journal of Production Research 58 (21) (2020) 6585–6600.
[45]
Zheng R., Wang Z., A generalized scalarization method for evolutionary multi-objective optimization, Proceedings of the AAAI conference on artificial intelligence, Vol. 37, 2023, pp. 12518–12525.
[46]
Zitzler E., Deb K., Thiele L., Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computation 8 (2) (2000) 173–195.
Index Terms
- Optimal computing budget allocation for selecting the optimal subset of multi-objective simulation optimization problems
Index terms have been assigned to the content through auto-classification.
Recommendations
Efficient simulation budget allocation for subset selection using regression metamodels
AbstractThis research considers the ranking and selection (R&S) problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly ...
Optimal computing budget allocation for the vector evaluated genetic algorithm in multi-objective simulation optimization
AbstractMotivated by the vector evaluation genetic algorithm (VEGA), this research develops simulation budget allocation rules for the VEGA in solving simulation optimization problems. We formulate the selection problem of the VEGA using the ...
Optimal budget allocation policy for tabu search in stochastic simulation optimization
AbstractTabu search (TS) is a powerful method for solving combinatorial optimization problems. However, when TS is adopted for stochastic simulation optimization, the simulation noises may mislead the search direction and prevent TS from ...
Highlights- Tabu search is misled by simulation noises in stochastic optimization problems.
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 21 November 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Dec 2024