IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v255y2016i1p121-132.html
   My bibliography  Save this article

An empirical analysis of scenario generation methods for stochastic optimization

Author

Listed:
  • Löhndorf, Nils

Abstract

This work presents an empirical analysis of popular scenario generation methods for stochastic optimization, including quasi-Monte Carlo, moment matching, and methods based on probability metrics, as well as a new method referred to as Voronoi cell sampling. Solution quality is assessed by measuring the error that arises from using scenarios to solve a multi-dimensional newsvendor problem, for which analytical solutions are available. In addition to the expected value, the work also studies scenario quality when minimizing the expected shortfall using the conditional value-at-risk. To quickly solve problems with millions of random parameters, a reformulation of the risk-averse newsvendor problem is proposed which can be solved via Benders decomposition. The empirical analysis identifies Voronoi cell sampling as the method that provides the lowest errors, with particularly good results for heavy-tailed distributions. A controversial finding concerns evidence for the ineffectiveness of widely used methods based on minimizing probability metrics under high-dimensional randomness.

Suggested Citation

  • Löhndorf, Nils, 2016. "An empirical analysis of scenario generation methods for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 255(1), pages 121-132.
    • Handle: RePEc:eee:ejores:v:255:y:2016:i:1:p:121-132
    DOI: 10.1016/j.ejor.2016.05.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716303411
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.05.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Allen Fleishman, 1978. "A method for simulating non-normal distributions," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 521-532, December.
    2. Gotoh, Jun-ya & Takano, Yuichi, 2007. "Newsvendor solutions via conditional value-at-risk minimization," European Journal of Operational Research, Elsevier, vol. 179(1), pages 80-96, May.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Julia L. Higle, 1998. "Variance Reduction and Objective Function Evaluation in Stochastic Linear Programs," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 236-247, May.
    5. Werner Jammernegg & Peter Kischka, 2007. "Risk-averse and risk-taking newsvendors: a conditional expected value approach," Review of Managerial Science, Springer, vol. 1(1), pages 93-110, April.
    6. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    7. Ronald Hochreiter & Georg Pflug, 2007. "Financial scenario generation for stochastic multi-stage decision processes as facility location problems," Annals of Operations Research, Springer, vol. 152(1), pages 257-272, July.
    8. Dias, Carlos Tadeu dos Santos & Samaranayaka, Ari & Manly, Bryan, 2008. "On the use of correlated beta random variables with animal population modelling," Ecological Modelling, Elsevier, vol. 215(4), pages 293-300.
    9. Michael Freimer & Jeffrey Linderoth & Douglas Thomas, 2012. "The impact of sampling methods on bias and variance in stochastic linear programs," Computational Optimization and Applications, Springer, vol. 51(1), pages 51-75, January.
    10. Marsaglia, George, 2003. "Xorshift RNGs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 8(i14).
    11. Michael A.H. Dempster & Elena A. Medova & Yee Sook Yong, 2011. "Comparison of Sampling Methods for Dynamic Stochastic Programming," International Series in Operations Research & Management Science, in: Marida Bertocchi & Giorgio Consigli & Michael A. H. Dempster (ed.), Stochastic Optimization Methods in Finance and Energy, edition 1, chapter 0, pages 389-425, Springer.
    12. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    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. Sa, Constantijn A.A. & Santos, Bruno F. & Clarke, John-Paul B., 2020. "Portfolio-based airline fleet planning under stochastic demand," Omega, Elsevier, vol. 97(C).

    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. Wu, Meng & Zhu, Stuart X. & Teunter, Ruud H., 2013. "The risk-averse newsvendor problem with random capacity," European Journal of Operational Research, Elsevier, vol. 231(2), pages 328-336.
    2. Xin-Sheng Xu & Felix T. S. Chan, 2019. "Optimal Option Purchasing Decisions for the Risk-Averse Retailer with Shortage Cost," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(02), pages 1-25, April.
    3. Wu, Meng & Zhu, Stuart X. & Teunter, Ruud H., 2014. "A risk-averse competitive newsvendor problem under the CVaR criterion," International Journal of Production Economics, Elsevier, vol. 156(C), pages 13-23.
    4. Marc Reimann & Philippe Schiltknecht, 2009. "The role of risk preferences and flexibility for risk management: lessons from a custom manufacturing environment," Review of Managerial Science, Springer, vol. 3(2), pages 117-140, July.
    5. Fan, Yinghua & Feng, Yi & Shou, Yongyi, 2020. "A risk-averse and buyer-led supply chain under option contract: CVaR minimization and channel coordination," International Journal of Production Economics, Elsevier, vol. 219(C), pages 66-81.
    6. Rebecca Stockbridge & Güzin Bayraksan, 2016. "Variance reduction in Monte Carlo sampling-based optimality gap estimators for two-stage stochastic linear programming," Computational Optimization and Applications, Springer, vol. 64(2), pages 407-431, June.
    7. Mario Brandtner, 2016. "Spektrale Risikomaße: Konzeption, betriebswirtschaftliche Anwendungen und Fallstricke," Management Review Quarterly, Springer, vol. 66(2), pages 75-115, April.
    8. Xu, Xinsheng & Ji, Ping & Sang, Shuming, 2023. "Supply option purchasing decisions via mismatch cost minimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 260-280.
    9. Xinsheng, Xu & Zhiqing, Meng & Rui, Shen & Min, Jiang & Ping, Ji, 2015. "Optimal decisions for the loss-averse newsvendor problem under CVaR," International Journal of Production Economics, Elsevier, vol. 164(C), pages 146-159.
    10. Li, Yanhai & Gu, Chaocheng & Ou, Jinwen, 2020. "Supporting a financially constrained supplier under spectral risk measures: The efficiency of buyer lending," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    11. Li, Yanhai & Ou, Jinwen, 2022. "Replenishment decisions for complementary components with supply capacity uncertainty under the CVaR criterion," European Journal of Operational Research, Elsevier, vol. 297(3), pages 904-916.
    12. Jangho Park & Rebecca Stockbridge & Güzin Bayraksan, 2021. "Variance reduction for sequential sampling in stochastic programming," Annals of Operations Research, Springer, vol. 300(1), pages 171-204, May.
    13. Qin, Yan & Wang, Ruoxuan & Vakharia, Asoo J. & Chen, Yuwen & Seref, Michelle M.H., 2011. "The newsvendor problem: Review and directions for future research," European Journal of Operational Research, Elsevier, vol. 213(2), pages 361-374, September.
    14. Li, Bo & Hou, Peng-Wen & Chen, Ping & Li, Qing-Hua, 2016. "Pricing strategy and coordination in a dual channel supply chain with a risk-averse retailer," International Journal of Production Economics, Elsevier, vol. 178(C), pages 154-168.
    15. D. Kuhn, 2009. "Convergent Bounds for Stochastic Programs with Expected Value Constraints," Journal of Optimization Theory and Applications, Springer, vol. 141(3), pages 597-618, June.
    16. Borgonovo, Emanuele & Gatti, Stefano, 2013. "Risk analysis with contractual default. Does covenant breach matter?," European Journal of Operational Research, Elsevier, vol. 230(2), pages 431-443.
    17. Hsieh, Chung-Chi & Lu, Yu-Ting, 2010. "Manufacturer's return policy in a two-stage supply chain with two risk-averse retailers and random demand," European Journal of Operational Research, Elsevier, vol. 207(1), pages 514-523, November.
    18. Brandtner, Mario, 2018. "Expected Shortfall, spectral risk measures, and the aggravating effect of background risk, or: risk vulnerability and the problem of subadditivity," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 138-149.
    19. Zhao, Lima & Huchzermeier, Arnd, 2017. "Integrated operational and financial hedging with capacity reshoring," European Journal of Operational Research, Elsevier, vol. 260(2), pages 557-570.
    20. Jianxin Chen & Yong-Wu Zhou, 2017. "A Risk-Averse Newsvendor Model Under Trade Credit Contract with CVaR," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-20, June.

    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:255:y:2016:i:1:p:121-132. 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.