IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v223y2014i1p309-32810.1007-s10479-013-1455-8.html
   My bibliography  Save this article

Stochastic vs deterministic programming in water management: the value of flexibility

Author

Listed:
  • Yousaf Muhammad
  • Georg Pflug

Abstract

In the paper we develop a two stage scenario-based stochastic programming model for water management in the Indus Basin Irrigation System (IBIS). We present a comparison between the deterministic and scenario-based stochastic programming model. Our model takes stochastic inputs on hydrologic data i.e. inflow and rainfall. We divide the basin into three rainfall zones which overlap on 44 canal commands. Data on crop characteristics are taken on canal command levels. We then use ten-daily and monthly time intervals to analyze the policies. This system has two major reservoirs and a complex network of rivers, canal head works, canals, sub canals and distributaries. All the decisions on hydrologic aspects are governed by irrigation and agricultural development policies. Storage levels are maintained within the minimum and maximum bounds for every time interval according to a power generation policy. The objective function is to maximize the expected revenue from crops production. We discuss the flexibility of two stochastic optimization models with varying time horizon. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Yousaf Muhammad & Georg Pflug, 2014. "Stochastic vs deterministic programming in water management: the value of flexibility," Annals of Operations Research, Springer, vol. 223(1), pages 309-328, December.
    • Handle: RePEc:spr:annopr:v:223:y:2014:i:1:p:309-328:10.1007/s10479-013-1455-8
    DOI: 10.1007/s10479-013-1455-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-013-1455-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-013-1455-8?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. Rao, N. H. & Sarma, P. B. S. & Chander, Subhash, 1992. "Real-time adaptive irrigation scheduling under a limited water supply," Agricultural Water Management, Elsevier, vol. 20(4), pages 267-279, February.
    2. 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.
    3. N. Umamahesh & P. Sreenivasulu, 1997. "Technical Communication: Two-Phase Stochastic Dynamic Programming Model for Optimal Operation of Irrigation Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(5), pages 395-406, October.
    4. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    5. Ullah, M. K., 2001. "Spatial distribution of reference and potential evapotranspiration across the Indus Basin Irrigation Systems," IWMI Working Papers H029426, International Water Management Institute.
    6. Oscar R. Burt & M. S. Stauber, 1971. "Economic Analysis of Irrigation in Subhumid Climate," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 53(1), pages 33-46.
    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. Reza Roozbahani & Babak Abbasi & Sergei Schreider & Zahra Hosseinifard, 2020. "A basin-wide approach for water allocation and dams location-allocation," Annals of Operations Research, Springer, vol. 287(1), pages 323-349, April.

    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. 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.
    2. Aghayi, Nazila & Maleki, Bentolhoda, 2016. "Efficiency measurement of DMUs with undesirable outputs under uncertainty based on the directional distance function: Application on bank industry," Energy, Elsevier, vol. 112(C), pages 376-387.
    3. J. F. F. Almeida & S. V. Conceição & L. R. Pinto & B. R. P. Oliveira & L. F. Rodrigues, 2022. "Optimal sales and operations planning for integrated steel industries," Annals of Operations Research, Springer, vol. 315(2), pages 773-790, August.
    4. Kanudia, Amit & Shukla, PR, 1998. "Modelling of Uncertainties and Price Elastic Demands in Energy-environment Planning for India," Omega, Elsevier, vol. 26(3), pages 409-423, June.
    5. 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.
    6. Arie M. C. A. Koster & Michael Poss, 2018. "Special issue on: robust combinatorial optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 207-209, September.
    7. Rashed Khanjani-Shiraz & Ali Babapour-Azar & Zohreh Hosseini-Noudeh & Panos M. Pardalos, 2022. "Distributionally robust maximum probability shortest path problem," Journal of Combinatorial Optimization, Springer, vol. 43(1), pages 140-167, January.
    8. Walid Ben-Ameur & Adam Ouorou & Guanglei Wang & Mateusz Żotkiewicz, 2018. "Multipolar robust optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 395-434, December.
    9. Lambert, David K. & McCarl, Bruce A. & He, Quifen & Kaylen, Michael S. & Rosenthal, Wesley & Chang, Ching-Cheng & Nayda, W.I., 1995. "Uncertain Yields In Sectoral Welfare Analysis: An Application To Global Warming," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 27(2), pages 1-14, December.
    10. Hsien-Chung Wu, 2019. "Numerical Method for Solving the Robust Continuous-Time Linear Programming Problems," Mathematics, MDPI, vol. 7(5), pages 1-50, May.
    11. Ketabchi, Saeed & Behboodi-Kahoo, Malihe, 2015. "Augmented Lagrangian method within L-shaped method for stochastic linear programs," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 12-20.
    12. Wang, Chengshan & Song, Guanyu & Li, Peng & Ji, Haoran & Zhao, Jinli & Wu, Jianzhong, 2017. "Optimal siting and sizing of soft open points in active electrical distribution networks," Applied Energy, Elsevier, vol. 189(C), pages 301-309.
    13. Ceren Tuncer Şakar & Murat Köksalan, 2013. "A stochastic programming approach to multicriteria portfolio optimization," Journal of Global Optimization, Springer, vol. 57(2), pages 299-314, October.
    14. Soonthornsima, Wipada, 1981. "Economic feasibility studies of irrigation in Northwest Iowa," ISU General Staff Papers 1981010108000018039, Iowa State University, Department of Economics.
    15. Alexander Engau, 2017. "Proper Efficiency and Tradeoffs in Multiple Criteria and Stochastic Optimization," Mathematics of Operations Research, INFORMS, vol. 42(1), pages 119-134, January.
    16. Kallio, Markku & Halme, Merja & Dehghan Hardoroudi, Nasim & Aspara, Jaakko, 2022. "Transparent structured products for retail investors," European Journal of Operational Research, Elsevier, vol. 302(2), pages 752-767.
    17. Zuo, Qiting & Wu, Qingsong & Yu, Lei & Li, Yongping & Fan, Yurui, 2021. "Optimization of uncertain agricultural management considering the framework of water, energy and food," Agricultural Water Management, Elsevier, vol. 253(C).
    18. Guanglei Wang & Hassan Hijazi, 2018. "Mathematical programming methods for microgrid design and operations: a survey on deterministic and stochastic approaches," Computational Optimization and Applications, Springer, vol. 71(2), pages 553-608, November.
    19. Pengyu Qian & Zizhuo Wang & Zaiwen Wen, 2015. "A Composite Risk Measure Framework for Decision Making under Uncertainty," Papers 1501.01126, arXiv.org.
    20. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.

    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:223:y:2014:i:1:p:309-328:10.1007/s10479-013-1455-8. 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.