IDEAS home Printed from https://ideas.repec.org/a/spr/topjnl/v15y2007i1p48-64.html
   My bibliography  Save this article

The value of the stochastic solution in multistage problems

Author

Listed:
  • Laureano Escudero
  • Araceli Garín
  • María Merino
  • Gloria Pérez

Abstract

No abstract is available for this item.

Suggested Citation

  • Laureano Escudero & Araceli Garín & María Merino & Gloria Pérez, 2007. "The value of the stochastic solution in multistage problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 48-64, July.
    • Handle: RePEc:spr:topjnl:v:15:y:2007:i:1:p:48-64
    DOI: 10.1007/s11750-007-0005-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11750-007-0005-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11750-007-0005-4?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. Hung-Po Chao, 1981. "Exhaustible Resource Models: The Value of Information," Operations Research, INFORMS, vol. 29(5), pages 903-923, October.
    2. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    3. Albert Madansky, 1960. "Inequalities for Stochastic Linear Programming Problems," Management Science, INFORMS, vol. 6(2), pages 197-204, January.
    Full references (including those not matched with items on IDEAS)

    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. Sheng-I Chen & Delvinia Su, 2022. "A multi-stage stochastic programming model of lot-sizing and scheduling problems with machine eligibilities and sequence-dependent setups," Annals of Operations Research, Springer, vol. 311(1), pages 35-50, April.
    2. Wu, Dexiang & Wu, Desheng Dash, 2020. "A decision support approach for two-stage multi-objective index tracking using improved lagrangian decomposition," Omega, Elsevier, vol. 91(C).
    3. 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.
    4. Fan, Yingjie & Schwartz, Frank & Voß, Stefan, 2017. "Flexible supply chain planning based on variable transportation modes," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 654-666.
    5. Barry C. Smith & Ellis L. Johnson, 2006. "Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition," Transportation Science, INFORMS, vol. 40(4), pages 497-516, November.
    6. Andreatta, Giovanni & Dell'Olmo, Paolo & Lulli, Guglielmo, 2011. "An aggregate stochastic programming model for air traffic flow management," European Journal of Operational Research, Elsevier, vol. 215(3), pages 697-704, December.
    7. Huang, Edward & Mital, Pratik & Goetschalckx, Marc & Wu, Kan, 2016. "Optimal assignment of airport baggage unloading zones to outgoing flights," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 110-122.
    8. Hu, Shaolong & Han, Chuanfeng & Dong, Zhijie Sasha & Meng, Lingpeng, 2019. "A multi-stage stochastic programming model for relief distribution considering the state of road network," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 64-87.
    9. Zhicheng Zhu & Yisha Xiang & Bo Zeng, 2021. "Multicomponent Maintenance Optimization: A Stochastic Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 898-914, July.
    10. Gregory A. Godfrey & Warren B. Powell, 2001. "An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution," Management Science, INFORMS, vol. 47(8), pages 1101-1112, August.
    11. Fadda, Edoardo & Manerba, Daniele & Cabodi, Gianpiero & Camurati, Paolo Enrico & Tadei, Roberto, 2021. "Comparative analysis of models and performance indicators for optimal service facility location," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    12. V.I. Norkin & G.C. Pflug & A. Ruszczynski, 1996. "A Branch and Bound Method for Stochastic Global Optimization," Working Papers wp96065, International Institute for Applied Systems Analysis.
    13. Riis, Morten & Andersen, Kim Allan, 2005. "Applying the minimax criterion in stochastic recourse programs," European Journal of Operational Research, Elsevier, vol. 165(3), pages 569-584, September.
    14. Jeffrey Christiansen & Brian Dandurand & Andrew Eberhard & Fabricio Oliveira, 2023. "A study of progressive hedging for stochastic integer programming," Computational Optimization and Applications, Springer, vol. 86(3), pages 989-1034, December.
    15. Alexander Franz & Julia Rieck & Jürgen Zimmermann, 2019. "Fix-and-optimize procedures for solving the long-term unit commitment problem with pumped storages," Annals of Operations Research, Springer, vol. 274(1), pages 241-265, March.
    16. Guo, Zhaomiao & Fan, Yueyue, 2017. "A Stochastic Multi-Agent Optimization Model for Energy Infrastructure Planning Under Uncertainty and Competition," Institute of Transportation Studies, Working Paper Series qt89s5s8hn, Institute of Transportation Studies, UC Davis.
    17. Davi Valladão & Thuener Silva & Marcus Poggi, 2019. "Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns," Annals of Operations Research, Springer, vol. 282(1), pages 379-405, November.
    18. Gyana R. Parija & Shabbir Ahmed & Alan J. King, 2004. "On Bridging the Gap Between Stochastic Integer Programming and MIP Solver Technologies," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 73-83, February.
    19. Douglas T. Gardner & J. Scott Rogers, 1999. "Planning Electric Power Systems Under Demand Uncertainty with Different Technology Lead Times," Management Science, INFORMS, vol. 45(10), pages 1289-1306, October.
    20. Yingjie Fan & Frank Schwartz & Stefan Voß & David L. Woodruff, 2017. "Stochastic programming for flexible global supply chain planning," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 601-633, December.

    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:spr:topjnl:v:15:y:2007:i:1:p:48-64. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.