IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v172y2006i3p761-782.html
   My bibliography  Save this article

A new approach to discrete stochastic optimization problems

Author

Listed:
  • Lin, Xiaocang
  • Lee, Loo Hay

Abstract

No abstract is available for this item.

Suggested Citation

  • Lin, Xiaocang & Lee, Loo Hay, 2006. "A new approach to discrete stochastic optimization problems," European Journal of Operational Research, Elsevier, vol. 172(3), pages 761-782, August.
  • Handle: RePEc:eee:ejores:v:172:y:2006:i:3:p:761-782
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(04)00849-5
    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. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, 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. H. Lu & G. Huang & Y. Lin & L. He, 2009. "A Two-Step Infinite α-Cuts Fuzzy Linear Programming Method in Determination of Optimal Allocation Strategies in Agricultural Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2249-2269, September.
    2. Andreas Deckert & Robert Klein, 2014. "Simulation-based optimization of an agent-based simulation," Netnomics, Springer, vol. 15(1), pages 33-56, July.

    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 & 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.
    2. Lingxuan Liu & Leyuan Shi, 2019. "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(05), pages 1-26, October.
    3. Zhenyuan Liu & Lei Xiao & Jing Tian, 2016. "An activity-list-based nested partitions algorithm for resource-constrained project scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4744-4758, August.
    4. K. Gokbayrak & C.G. Cassandras, 2002. "Generalized Surrogate Problem Methodology for Online Stochastic Discrete Optimization," Journal of Optimization Theory and Applications, Springer, vol. 114(1), pages 97-132, July.
    5. K. Gokbayrak & C. G. Cassandras, 2001. "Online Surrogate Problem Methodology for Stochastic Discrete Resource Allocation Problems," Journal of Optimization Theory and Applications, Springer, vol. 108(2), pages 349-376, February.
    6. Xiao-Ming Yang & Xin-Jia Jiang, 2020. "Yard Crane Scheduling in the Ground Trolley-Based Automated Container Terminal," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(02), pages 1-28, March.
    7. Leyuan Shi & Sigurdur O´lafsson, 2000. "Nested Partitions Method for Stochastic Optimization," Methodology and Computing in Applied Probability, Springer, vol. 2(3), pages 271-291, September.
    8. Szu Hui Ng & Stephen E. Chick, 2004. "Design of follow‐up experiments for improving model discrimination and parameter estimation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(8), pages 1129-1148, December.
    9. Fan, Qi & Tan, Ken Seng & Zhang, Jinggong, 2023. "Empirical tail risk management with model-based annealing random search," Insurance: Mathematics and Economics, Elsevier, vol. 110(C), pages 106-124.
    10. Chen Wang & Vicki M. Bier, 2011. "Target-Hardening Decisions Based on Uncertain Multiattribute Terrorist Utility," Decision Analysis, INFORMS, vol. 8(4), pages 286-302, December.
    11. G. Alon & D. Kroese & T. Raviv & R. Rubinstein, 2005. "Application of the Cross-Entropy Method to the Buffer Allocation Problem in a Simulation-Based Environment," Annals of Operations Research, Springer, vol. 134(1), pages 137-151, February.
    12. Grishagin, Vladimir & Israfilov, Ruslan & Sergeyev, Yaroslav, 2018. "Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 270-280.
    13. Alfredo Garcia & Stephen D. Patek & Kaushik Sinha, 2007. "A Decentralized Approach to Discrete Optimization via Simulation: Application to Network Flow," Operations Research, INFORMS, vol. 55(4), pages 717-732, August.
    14. J Yang & S Ólafsson, 2009. "Near-optimal feature selection for large databases," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1045-1055, August.
    15. Xiquan Wang & Xingdong Zhang & Xiaohu Liu & Lijie Guo & Thomas Li & Jin Dong & Wenjun Yin & Ming Xie & Bin Zhang, 2012. "Branch Reconfiguration Practice Through Operations Research in Industrial and Commercial Bank of China," Interfaces, INFORMS, vol. 42(1), pages 33-44, February.
    16. Xinghua Tao & Nan Mo & Jianbo Qin & Xiaozhe Yang & Linfei Yin & Likun Hu, 2023. "Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization," Energies, MDPI, vol. 16(19), pages 1-20, October.
    17. Qi Zhang & Jiaqiao Hu, 2019. "Simulation Optimization Using Multi-Time-Scale Adaptive Random Search," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(06), pages 1-34, December.
    18. Chang, Kuo-Hao & Kuo, Po-Yi, 2018. "An efficient simulation optimization method for the generalized redundancy allocation problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1094-1101.
    19. David R. Morrison & Jason J. Sauppe & Wenda Zhang & Sheldon H. Jacobson & Edward C. Sewell, 2017. "Cyclic best first search: Using contours to guide branch‐and‐bound algorithms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(1), pages 64-82, February.
    20. Tahir Ekin & Stephen Walker & Paul Damien, 2023. "Augmented simulation methods for discrete stochastic optimization with recourse," Annals of Operations Research, Springer, vol. 320(2), pages 771-793, January.

    More about this item

    Statistics

    Access and download statistics

    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:172:y:2006:i:3:p:761-782. 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.