IDEAS home Printed from https://ideas.repec.org/a/spr/aqjoor/v19y2021i4d10.1007_s10288-020-00462-x.html
   My bibliography  Save this article

Comparing stage-scenario with nodal formulation for multistage stochastic problems

Author

Listed:
  • Sebastiano Vitali

    (Charles University
    University of Bergamo)

  • Ruth Domínguez

    (University of Castilla – La Mancha)

  • Vittorio Moriggia

    (University of Bergamo)

Abstract

To solve real life problems under uncertainty in Economics, Finance, Energy, Transportation and Logistics, the use of stochastic optimization is widely accepted and appreciated. However, the nature of stochastic programming leads to a conflict between adaptability to reality and tractability. To formulate a multistage stochastic model, two types of formulations are typically adopted: the so-called stage-scenario formulation named also formulation with explicit non-anticipativity constraints and the so-called nodal formulation named also formulation with implicit non-anticipativity constraints. Both of them have advantages and disadvantages. This work aims at helping the scholars and practitioners to understand the two types of notation and, in particular, to reformulate with the nodal formulation a model that was originally defined with the stage-scenario formulation presenting this implementation in the algebraic language GAMS. In addition, this work presents an empirical analysis applying the two formulations both without any further decomposition to perform a fair comparison. In this way, we show that the difficulties to implement the model with the nodal formulation are somehow reworded making the problem tractable without any decomposition algorithm. Still, we remark that in some other applications the stage-scenario formulation could be more helpful to understand the structure of the problem since it allows to relax the non-anticipativity constraints.

Suggested Citation

  • Sebastiano Vitali & Ruth Domínguez & Vittorio Moriggia, 2021. "Comparing stage-scenario with nodal formulation for multistage stochastic problems," 4OR, Springer, vol. 19(4), pages 613-631, December.
  • Handle: RePEc:spr:aqjoor:v:19:y:2021:i:4:d:10.1007_s10288-020-00462-x
    DOI: 10.1007/s10288-020-00462-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10288-020-00462-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10288-020-00462-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. Giorgio Consigli & Massimo di Tria & Michele Gaffo & Gaetano Iaquinta & Vittorio Moriggia & Angelo Uristani, 2011. "Dynamic Portfolio Management for Property and Casualty Insurance," 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 99-124, Springer.
    2. Kouwenberg, Roy, 2001. "Scenario generation and stochastic programming models for asset liability management," European Journal of Operational Research, Elsevier, vol. 134(2), pages 279-292, October.
    3. Miloš Kopa & Vittorio Moriggia & Sebastiano Vitali, 2018. "Individual optimal pension allocation under stochastic dominance constraints," Annals of Operations Research, Springer, vol. 260(1), pages 255-291, January.
    4. David R. Cariño & Terry Kent & David H. Myers & Celine Stacy & Mike Sylvanus & Andrew L. Turner & Kouji Watanabe & William T. Ziemba, 1994. "The Russell-Yasuda Kasai Model: An Asset/Liability Model for a Japanese Insurance Company Using Multistage Stochastic Programming," Interfaces, INFORMS, vol. 24(1), pages 29-49, February.
    5. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    6. 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.
    7. Giorgio Consigli & Vittorio Moriggia & Elena Benincasa & Giacomo Landoni & Filomena Petronio & Sebastiano Vitali & Massimo di Tria & Mario Skoric & Angelo Uristani, 2018. "Optimal Multistage Defined-Benefit Pension Fund Management," International Series in Operations Research & Management Science, in: Giorgio Consigli & Silvana Stefani & Giovanni Zambruno (ed.), Handbook of Recent Advances in Commodity and Financial Modeling, chapter 0, pages 267-296, Springer.
    8. Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
    9. Vittorio Moriggia & Giorgio Consigli & Gaetano Iaquinta, 2013. "Optimal Stochastic Programming-Based Personal Financial Planning with Intermediate and Long-Term Goals," World Scientific Book Chapters, in: Horand I Gassmann & William T Ziemba (ed.), Stochastic Programming Applications in Finance, Energy, Planning and Logistics, chapter 3, pages 43-68, World Scientific Publishing Co. Pte. Ltd..
    10. Sebastiano Vitali & Vittorio Moriggia & Miloš Kopa, 2017. "Optimal pension fund composition for an Italian private pension plan sponsor," Computational Management Science, Springer, vol. 14(1), pages 135-160, January.
    11. Jitka Dupačová & Marida Bertocchi & Vittorio Moriggia, 2009. "Testing the structure of multistage stochastic programs," Computational Management Science, Springer, vol. 6(2), pages 161-185, May.
    12. G. Consigli & M. Dempster, 1998. "Dynamic stochastic programmingfor asset-liability management," Annals of Operations Research, Springer, vol. 81(0), pages 131-162, June.
    13. Moriggia, Vittorio & Kopa, Miloš & Vitali, Sebastiano, 2019. "Pension fund management with hedging derivatives, stochastic dominance and nodal contamination," Omega, Elsevier, vol. 87(C), pages 127-141.
    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. Meersman, Tine & Maenhout, Broos & Van Herck, Koen, 2023. "A nested Benders decomposition-based algorithm to solve the three-stage stochastic optimisation problem modeling population-based breast cancer screening," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1273-1293.

    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. Sebastiano Vitali & Vittorio Moriggia, 2021. "Pension fund management with investment certificates and stochastic dominance," Annals of Operations Research, Springer, vol. 299(1), pages 273-292, April.
    2. Moriggia, Vittorio & Kopa, Miloš & Vitali, Sebastiano, 2019. "Pension fund management with hedging derivatives, stochastic dominance and nodal contamination," Omega, Elsevier, vol. 87(C), pages 127-141.
    3. Sebastiano Vitali & Vittorio Moriggia & Miloš Kopa, 2017. "Optimal pension fund composition for an Italian private pension plan sponsor," Computational Management Science, Springer, vol. 14(1), pages 135-160, January.
    4. ManMohan S. Sodhi, 2005. "LP Modeling for Asset-Liability Management: A Survey of Choices and Simplifications," Operations Research, INFORMS, vol. 53(2), pages 181-196, April.
    5. Miloš Kopa & Tomáš Rusý, 2021. "A decision-dependent randomness stochastic program for asset–liability management model with a pricing decision," Annals of Operations Research, Springer, vol. 299(1), pages 241-271, April.
    6. Jacek Gondzio & Roy Kouwenberg, 2001. "High-Performance Computing for Asset-Liability Management," Operations Research, INFORMS, vol. 49(6), pages 879-891, December.
    7. Markéta Horejšová & Sebastiano Vitali & Miloš Kopa & Vittorio Moriggia, 2020. "Evaluation of scenario reduction algorithms with nested distance," Computational Management Science, Springer, vol. 17(2), pages 241-275, June.
    8. Arjan Berkelaar & Roy Kouwenberg, 2011. "A Liability-Relative Drawdown Approach to Pension Asset Liability Management," Palgrave Macmillan Books, in: Gautam Mitra & Katharina Schwaiger (ed.), Asset and Liability Management Handbook, chapter 14, pages 352-382, Palgrave Macmillan.
    9. Xie, Fei & Huang, Yongxi, 2018. "A multistage stochastic programming model for a multi-period strategic expansion of biofuel supply chain under evolving uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 130-148.
    10. Giorgio Consigli & Vittorio Moriggia & Sebastiano Vitali & Lorenzo Mercuri, 2018. "Optimal insurance portfolios risk-adjusted performance through dynamic stochastic programming," Computational Management Science, Springer, vol. 15(3), pages 599-632, October.
    11. Maram Alwohaibi & Diana Roman, 2018. "ALM models based on second order stochastic dominance," Computational Management Science, Springer, vol. 15(2), pages 187-211, June.
    12. 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).
    13. Giorgio Consigli & Vittorio Moriggia & Sebastiano Vitali, 2020. "Long-term individual financial planning under stochastic dominance constraints," Annals of Operations Research, Springer, vol. 292(2), pages 973-1000, September.
    14. 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.
    15. Marco Di Francesco & Roberta Simonella, 2023. "A stochastic Asset Liability Management model for life insurance companies," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(1), pages 61-94, March.
    16. Julia Higle & Suvrajeet Sen, 2006. "Multistage stochastic convex programs: Duality and its implications," Annals of Operations Research, Springer, vol. 142(1), pages 129-146, February.
    17. John M. Mulvey & Koray D. Simsek & Zhuojuan Zhang & Frank J. Fabozzi & William R. Pauling, 2008. "OR PRACTICE---Assisting Defined-Benefit Pension Plans," Operations Research, INFORMS, vol. 56(5), pages 1066-1078, October.
    18. Sodhi, ManMohan S. & Tang, Christopher S., 2009. "Modeling supply-chain planning under demand uncertainty using stochastic programming: A survey motivated by asset-liability management," International Journal of Production Economics, Elsevier, vol. 121(2), pages 728-738, October.
    19. Arjen Siegmann & André Lucas, 2005. "Discrete-Time Financial Planning Models Under Loss-Averse Preferences," Operations Research, INFORMS, vol. 53(3), pages 403-414, June.
    20. Giovanni Pantuso & Trine K. Boomsma, 2020. "On the number of stages in multistage stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 581-603, September.

    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:aqjoor:v:19:y:2021:i:4:d:10.1007_s10288-020-00462-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.