IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v166y2009i1p313-33710.1007-s10479-008-0419-x.html
   My bibliography  Save this article

An XML-based schema for stochastic programs

Author

Listed:
  • R. Fourer
  • H. Gassmann
  • J. Ma
  • R. Martin

Abstract

This paper describes a proposed format to record instances of stochastic programs. It forms part of a larger XML-based schema that is designed to allow the expression of essentially any type of mathematical program within a unifying framework. A wide variety of different linear and nonlinear stochastic programs can be handled, and the paper describes in some detail how this is done. Screen captures and sample problems illustrate the use of the schema. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • R. Fourer & H. Gassmann & J. Ma & R. Martin, 2009. "An XML-based schema for stochastic programs," Annals of Operations Research, Springer, vol. 166(1), pages 313-337, February.
  • Handle: RePEc:spr:annopr:v:166:y:2009:i:1:p:313-337:10.1007/s10479-008-0419-x
    DOI: 10.1007/s10479-008-0419-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-008-0419-x
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10479-008-0419-x?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. H.I. Gassmann & E. Schweitzer, 2001. "A Comprehensive Input Format for Stochastic Linear Programs," Annals of Operations Research, Springer, vol. 104(1), pages 89-125, April.
    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. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    4. D. Klingman & A. Napier & J. Stutz, 1974. "NETGEN: A Program for Generating Large Scale Capacitated Assignment, Transportation, and Minimum Cost Flow Network Problems," Management Science, INFORMS, vol. 20(5), pages 814-821, January.
    5. Dentcheva, Darinka & Ruszczynski, Andrzej, 2006. "Portfolio optimization with stochastic dominance constraints," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 433-451, February.
    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. Olivier Cailloux & Tommi Tervonen & Boris Verhaegen & François Picalausa, 2014. "A data model for algorithmic multiple criteria decision analysis," Annals of Operations Research, Springer, vol. 217(1), pages 77-94, June.
    2. Robert Fourer & Jun Ma & Kipp Martin, 2010. "Optimization Services: A Framework for Distributed Optimization," Operations Research, INFORMS, vol. 58(6), pages 1624-1636, December.

    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. Zhou, Feng & Huang, Gordon H. & Chen, Guo-Xian & Guo, Huai-Cheng, 2009. "Enhanced-interval linear programming," European Journal of Operational Research, Elsevier, vol. 199(2), pages 323-333, December.
    2. Xi Yang & Jacek Gondzio & Andreas Grothey, 2010. "Asset liability management modelling with risk control by stochastic dominance," Journal of Asset Management, Palgrave Macmillan, vol. 11(2), pages 73-93, June.
    3. Maram Alwohaibi & Diana Roman, 2018. "ALM models based on second order stochastic dominance," Computational Management Science, Springer, vol. 15(2), pages 187-211, June.
    4. Robert Fourer & Leo Lopes, 2009. "StAMPL: A Filtration-Oriented Modeling Tool for Multistage Stochastic Recourse Problems," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 242-256, May.
    5. Álvarez-Miranda, Eduardo & Garcia-Gonzalo, Jordi & Ulloa-Fierro, Felipe & Weintraub, Andrés & Barreiro, Susana, 2018. "A multicriteria optimization model for sustainable forest management under climate change uncertainty: An application in Portugal," European Journal of Operational Research, Elsevier, vol. 269(1), pages 79-98.
    6. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    7. Gutierrez, Genaro J. & Kouvelis, Panagiotis & Kurawarwala, Abbas A., 1996. "A robustness approach to uncapacitated network design problems," European Journal of Operational Research, Elsevier, vol. 94(2), pages 362-376, October.
    8. Anissa Chaibi & Maria-Lenuta Ciupac-Ulici & Mircea-Cristian Gherman, 2014. "Do Recent Stochastic Tools Help to Better Understand Investors Preference and Asset Allocation?," Working Papers 2014-130, Department of Research, Ipag Business School.
    9. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    10. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    11. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    12. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    13. Lee, Jinkyu & Bae, Sanghyeon & Kim, Woo Chang & Lee, Yongjae, 2023. "Value function gradient learning for large-scale multistage stochastic programming problems," European Journal of Operational Research, Elsevier, vol. 308(1), pages 321-335.
    14. Hermann Held, 2019. "Cost Risk Analysis: Dynamically Consistent Decision-Making under Climate Targets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 247-261, January.
    15. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    16. Minghe Sun, 2005. "Warm-Start Routines for Solving Augmented Weighted Tchebycheff Network Programs in Multiple-Objective Network Programming," INFORMS Journal on Computing, INFORMS, vol. 17(4), pages 422-437, November.
    17. Lars M. Hvattum & Arne Løkketangen & Gilbert Laporte, 2006. "Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic," Transportation Science, INFORMS, vol. 40(4), pages 421-438, November.
    18. Xin Huang & Duan Li & Daniel Zhuoyu Long, 2020. "Scenario-decomposition Solution Framework for Nonseparable Stochastic Control Problems," Papers 2010.08985, arXiv.org.
    19. Özgün Elçi & John Hooker, 2022. "Stochastic Planning and Scheduling with Logic-Based Benders Decomposition," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2428-2442, September.
    20. Wu, Desheng (Dash) & Lee, Chi-Guhn, 2010. "Stochastic DEA with ordinal data applied to a multi-attribute pricing problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1679-1688, December.

    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:spr:annopr:v:166:y:2009:i:1:p:313-337:10.1007/s10479-008-0419-x. 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.