IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v203y2010i2p419-429.html
   My bibliography  Save this article

Integration of indifference-zone with multi-objective computing budget allocation

Author

Listed:
  • Teng, Suyan
  • Lee, Loo Hay
  • Chew, Ek Peng

Abstract

In this paper, we consider how to address the issues of having designs with close performance in the multi-objective ranking and selection (MORS) problem. To resolve this issue we propose integrating the indifference-zone (IZ) concept into the multi-objective computing budget allocation (MOCBA) framework. In particular, when IZ is introduced into the MOCBA framework, we address how to determine the probability of non-dominance, how to define the Pareto set, and how to derive allocation rules for the simulation replications. Empirical results show that the MOCBA framework with IZ can significantly save simulation budget when designs to be compared have close performance.

Suggested Citation

  • Teng, Suyan & Lee, Loo Hay & Chew, Ek Peng, 2010. "Integration of indifference-zone with multi-objective computing budget allocation," European Journal of Operational Research, Elsevier, vol. 203(2), pages 419-429, June.
  • Handle: RePEc:eee:ejores:v:203:y:2010:i:2:p:419-429
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(09)00548-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, E. Jack & Kelton, W. David, 2005. "Sequential selection procedures: Using sample means to improve efficiency," European Journal of Operational Research, Elsevier, vol. 166(1), pages 133-153, October.
    2. Barry L. Nelson & Julie Swann & David Goldsman & Wheyming Song, 2001. "Simple Procedures for Selecting the Best Simulated System When the Number of Alternatives is Large," Operations Research, INFORMS, vol. 49(6), pages 950-963, December.
    3. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2004. "Selecting the best stochastic system for large scale problems in DEDS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(2), pages 237-245.
    4. Thabane, Lehana & Drekic, Steve, 2003. "Hypothesis testing for the generalized multivariate modified Bessel model," Journal of Multivariate Analysis, Elsevier, vol. 86(2), pages 360-374, August.
    5. Gupta, Arjun K. & Harrar, Solomon W. & Fujikoshi, Yasunori, 2006. "Asymptotics for testing hypothesis in some multivariate variance components model under non-normality," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 148-178, January.
    6. John Butler & Douglas J. Morrice & Peter W. Mullarkey, 2001. "A Multiple Attribute Utility Theory Approach to Ranking and Selection," Management Science, INFORMS, vol. 47(6), pages 800-816, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yoon, Moonyoung & Bekker, James, 2019. "Considering sample means in Rinott’s procedure with a Bayesian approach," European Journal of Operational Research, Elsevier, vol. 273(1), pages 249-258.
    2. Wang, Tianxiang & Xu, Jie & Hu, Jian-Qiang & Chen, Chun-Hung, 2023. "Efficient estimation of a risk measure requiring two-stage simulation optimization," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1355-1365.
    3. Healey, Christopher M. & Andradóttir, Sigrún & Kim, Seong-Hee, 2013. "Efficient comparison of constrained systems using dormancy," European Journal of Operational Research, Elsevier, vol. 224(2), pages 340-352.
    4. Mattila, V. & Virtanen, K., 2015. "Ranking and selection for multiple performance measures using incomplete preference information," European Journal of Operational Research, Elsevier, vol. 242(2), pages 568-579.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Loo Hay & Chew, Ek Peng & Teng, Suyan & Chen, Yankai, 2008. "Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem," European Journal of Operational Research, Elsevier, vol. 189(2), pages 476-491, September.
    2. Lee, Loo Hay & Chew, Ek Peng & Manikam, Puvaneswari, 2006. "A general framework on the simulation-based optimization under fixed computing budget," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1828-1841, November.
    3. Jürgen Branke & Stephen E. Chick & Christian Schmidt, 2007. "Selecting a Selection Procedure," Management Science, INFORMS, vol. 53(12), pages 1916-1932, December.
    4. Alrefaei, Mahmoud H. & Alawneh, Ameen J., 2005. "Solution quality of random search methods for discrete stochastic optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(2), pages 115-125.
    5. Healey, Christopher M. & Andradóttir, Sigrún & Kim, Seong-Hee, 2013. "Efficient comparison of constrained systems using dormancy," European Journal of Operational Research, Elsevier, vol. 224(2), pages 340-352.
    6. Yoon, Moonyoung & Bekker, James, 2019. "Considering sample means in Rinott’s procedure with a Bayesian approach," European Journal of Operational Research, Elsevier, vol. 273(1), pages 249-258.
    7. Sigrún Andradóttir & Seong‐Hee Kim, 2010. "Fully sequential procedures for comparing constrained systems via simulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(5), pages 403-421, August.
    8. Tsai, Shing Chih, 2011. "Selecting the best simulated system with weighted control-variate estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 705-717.
    9. Shing Chih Tsai & Wu Hung Lin & Chia Cheng Wu & Shao Jen Weng & Ching Fen Tang, 2022. "Decision support algorithms for optimizing surgery start times considering the performance variation," Health Care Management Science, Springer, vol. 25(2), pages 208-221, June.
    10. Emre Barut & Warren Powell, 2014. "Optimal learning for sequential sampling with non-parametric beliefs," Journal of Global Optimization, Springer, vol. 58(3), pages 517-543, March.
    11. Mady, Afaf M., 2006. "Some extensions of Langenberg model for clinical trials with delayed observations normally distributed responses," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1384-1392, November.
    12. MacKenzie, Cameron A. & Hu, Chao, 2019. "Decision making under uncertainty for design of resilient engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    13. Gutjahr, Walter J. & Katzensteiner, Stefan & Reiter, Peter & Stummer, Christian & Denk, Michaela, 2010. "Multi-objective decision analysis for competence-oriented project portfolio selection," European Journal of Operational Research, Elsevier, vol. 205(3), pages 670-679, September.
    14. Chao Fu & Weiyong Liu & Wenjun Chang, 2020. "Data-driven multiple criteria decision making for diagnosis of thyroid cancer," Annals of Operations Research, Springer, vol. 293(2), pages 833-862, October.
    15. Tsai, Shing Chih & Chen, Sin Ting, 2017. "A simulation-based multi-objective optimization framework: A case study on inventory management," Omega, Elsevier, vol. 70(C), pages 148-159.
    16. Peter Mullarkey & Grant Butler & Srinagesh Gavirneni & Douglas Morrice, 2007. "Schlumberger Uses Simulation in Bidding and Executing Land Seismic Surveys," Interfaces, INFORMS, vol. 37(2), pages 120-132, April.
    17. Jie Xu & Barry L. Nelson & L. Jeff Hong, 2013. "An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 133-146, February.
    18. Olcay Arslan, 2015. "Variance-mean mixture of the multivariate skew normal distribution," Statistical Papers, Springer, vol. 56(2), pages 353-378, May.
    19. Jack Chen, E., 2011. "A revisit of two-stage selection procedures," European Journal of Operational Research, Elsevier, vol. 210(2), pages 281-286, April.
    20. L. Jeff Hong & Jun Luo & Barry L. Nelson, 2015. "Chance Constrained Selection of the Best," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 317-334, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:203:y:2010:i:2:p:419-429. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.