IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v216y2012i1p214-224.html
   My bibliography  Save this article

Cost/risk balanced management of scarce resources using stochastic programming

Author

Listed:
  • Gaivoronski, Alexei
  • Sechi, Giovanni M.
  • Zuddas, Paola

Abstract

We consider the situation when a scarce renewable resource should be periodically distributed between different users by a Resource Management Authority (RMA). The replenishment of this resource as well as users demand is subject to considerable uncertainty. We develop cost optimization and risk management models that can assist the RMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. These models are based on utilization and further development of the general methodology of stochastic programming for scenario optimization, taking into account appropriate risk management approaches. By a scenario optimization model we obtain a target barycentric value with respect to selected decision variables. A successive reoptimization of deterministic model for the worst case scenarios allows the reduction of the risk of negative consequences derived from unmet resources demand. Our reference case study is the distribution of scarce water resources. We show results of some numerical experiments in real physical systems.

Suggested Citation

  • Gaivoronski, Alexei & Sechi, Giovanni M. & Zuddas, Paola, 2012. "Cost/risk balanced management of scarce resources using stochastic programming," European Journal of Operational Research, Elsevier, vol. 216(1), pages 214-224.
    • Handle: RePEc:eee:ejores:v:216:y:2012:i:1:p:214-224
    DOI: 10.1016/j.ejor.2011.06.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221711005698
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2011.06.040?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. Mariano-Romero, Carlos E. & Alcocer-Yamanaka, Victor H. & Morales, Eduardo F., 2007. "Multi-objective optimization of water-using systems," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1691-1707, September.
    2. Alexei Gaivoronski & Petter de Lange, 2000. "An Asset Liability Management Model for Casualty Insurers: Complexity Reduction vs. Parameterized Decision Rules," Annals of Operations Research, Springer, vol. 99(1), pages 227-250, December.
    3. Maqsood, Imran & Huang, Guo H. & Scott Yeomans, Julian, 2005. "An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 208-225, November.
    4. 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.
    5. C Lucas & S A MirHassani & G Mitra & C A Poojari, 2001. "An application of Lagrangian relaxation to a capacity planning problem under uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(11), pages 1256-1266, November.
    6. Bravo, Mila & Gonzalez, Ignacio, 2009. "Applying stochastic goal programming: A case study on water use planning," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1123-1129, August.
    7. David R. Cariño & William T. Ziemba, 1998. "Formulation of the Russell-Yasuda Kasai Financial Planning Model," Operations Research, INFORMS, vol. 46(4), pages 433-449, August.
    8. Dupacova, Jitka & Gaivoronski, Alexei & Kos, Zdenek & Szantai, Tamas, 1991. "Stochastic programming in water management: A case study and a comparison of solution techniques," European Journal of Operational Research, Elsevier, vol. 52(1), pages 28-44, May.
    9. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    10. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    11. Di Francesco, Massimo & Crainic, Teodor Gabriel & Zuddas, Paola, 2009. "The effect of multi-scenario policies on empty container repositioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 758-770, September.
    12. Azaiez, M. N. & Hariga, M., 2001. "A single-period model for conjunctive use of ground and surface water under severe overdrafts and water deficit," European Journal of Operational Research, Elsevier, vol. 133(3), pages 653-666, September.
    13. Hajkowicz, Stefan & Higgins, Andrew, 2008. "A comparison of multiple criteria analysis techniques for water resource management," European Journal of Operational Research, Elsevier, vol. 184(1), pages 255-265, January.
    14. Gregory D. Glockner & George L. Nemhauser, 2000. "A Dynamic Network Flow Problem with Uncertain arc Capacities: Formulation and Problem Structure," Operations Research, INFORMS, vol. 48(2), pages 233-242, April.
    15. Cervellera, Cristiano & Chen, Victoria C.P. & Wen, Aihong, 2006. "Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1139-1151, June.
    16. Huang, G. H., 1998. "A hybrid inexact-stochastic water management model," European Journal of Operational Research, Elsevier, vol. 107(1), pages 137-158, May.
    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. Siqian Shen & Mingdi You & Yintai Ma, 2017. "Single‐commodity stochastic network design under demand and topological uncertainties with insufficient data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(2), pages 154-173, March.
    2. Sangjong Han & Hwankook Hwang & Seonghoon Kim & Gyu Seok Baek & Joonhong Park, 2015. "Sustainable Water Infrastructure Asset Management: A Gap Analysis of Customer and Service Provider Perspectives," Sustainability, MDPI, vol. 7(10), pages 1-17, September.
    3. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    4. Sobhani, A. & Wahab, M.I.M. & Neumann, W.P., 2017. "Incorporating human factors-related performance variation in optimizing a serial system," European Journal of Operational Research, Elsevier, vol. 257(1), pages 69-83.
    5. Wang, S. & Huang, G.H., 2016. "Risk-based factorial probabilistic inference for optimization of flood control systems with correlated uncertainties," European Journal of Operational Research, Elsevier, vol. 249(1), pages 258-269.
    6. Ahmed, Sajjad & Elsholkami, Mohamed & Elkamel, Ali & Du, Juan & Ydstie, Erik B. & Douglas, Peter L., 2014. "Financial risk management for new technology integration in energy planning under uncertainty," Applied Energy, Elsevier, vol. 128(C), pages 75-81.
    7. Wang, S. & Huang, G.H., 2015. "A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management," European Journal of Operational Research, Elsevier, vol. 240(2), pages 572-581.
    8. Alonso-Ayuso, Antonio & Carvallo, Felipe & Escudero, Laureano F. & Guignard, Monique & Pi, Jiaxing & Puranmalka, Raghav & Weintraub, Andrés, 2014. "Medium range optimization of copper extraction planning under uncertainty in future copper prices," European Journal of Operational Research, Elsevier, vol. 233(3), pages 711-726.
    9. Mori, Masakatsu & Kobayashi, Ryoji & Samejima, Masaki & Komoda, Norihisa, 2017. "Risk-cost optimization for procurement planning in multi-tier supply chain by Pareto Local Search with relaxed acceptance criterion," European Journal of Operational Research, Elsevier, vol. 261(1), pages 88-96.
    10. Wencong Yue & Zhongqi Liu & Meirong Su & Meng Xu & Qiangqiang Rong & Chao Xu & Zhenkun Tan & Xuming Jiang & Zhixin Su & Yanpeng Cai, 2022. "Inclusion of Ecological Water Requirements in Optimization of Water Resource Allocation Under Changing Climatic Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 551-570, January.
    11. Sangjong Han & Dan Daehyun Koo & Youngkyung Kim & Seonghoon Kim & Joonhong Park, 2017. "Gap Analysis Based Decision Support Methodology to Improve Level of Service of Water Services," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    12. Mitra, Sovan & Karathanasopoulos, Andreas & Sermpinis, Georgios & Dunis, Christian & Hood, John, 2015. "Operational risk: Emerging markets, sectors and measurement," European Journal of Operational Research, Elsevier, vol. 241(1), pages 122-132.
    13. Giovanni M. Sechi & Alexei A. Gaivoronski & Jacopo Napolitano, 2019. "Optimising Pumping Activation in Multi-Reservoir Water Supply Systems under Uncertainty with Stochastic Quasi-Gradient Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1881-1895, March.

    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. 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.
    2. 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.
    3. Wang, S. & Huang, G.H., 2015. "A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management," European Journal of Operational Research, Elsevier, vol. 240(2), pages 572-581.
    4. 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.
    5. Li, Y.P. & Huang, G.H. & Yang, Z.F. & Nie, S.L., 2008. "IFMP: Interval-fuzzy multistage programming for water resources management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 52(5), pages 800-812.
    6. Gauvin, Charles & Delage, Erick & Gendreau, Michel, 2017. "Decision rule approximations for the risk averse reservoir management problem," European Journal of Operational Research, Elsevier, vol. 261(1), pages 317-336.
    7. X. Qin & G. Huang, 2009. "An Inexact Chance-constrained Quadratic Programming Model for Stream Water Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 661-695, March.
    8. Chen, Shu & Shao, Dongguo & Tan, Xuezhi & Gu, Wenquan & Lei, Caixiu, 2017. "An interval multistage classified model for regional inter- and intra-seasonal water management under uncertain and nonstationary condition," Agricultural Water Management, Elsevier, vol. 191(C), pages 98-112.
    9. 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).
    10. Ferstl, Robert & Weissensteiner, Alex, 2011. "Asset-liability management under time-varying investment opportunities," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 182-192, January.
    11. 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.
    12. Topaloglou, Nikolas & Vladimirou, Hercules & Zenios, Stavros A., 2020. "Integrated dynamic models for hedging international portfolio risks," European Journal of Operational Research, Elsevier, vol. 285(1), pages 48-65.
    13. Trine K. Boomsma, 2019. "Comments on: A comparative study of time aggregation techniques in relation to power capacity-expansion modeling," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 406-409, October.
    14. Li, Y.P. & Huang, G.H. & Zhang, N. & Nie, S.L., 2011. "An inexact-stochastic with recourse model for developing regional economic-ecological sustainability under uncertainty," Ecological Modelling, Elsevier, vol. 222(2), pages 370-379.
    15. Li, Y.P. & Huang, G.H. & Nie, S.L. & Chen, X., 2011. "A robust modeling approach for regional water management under multiple uncertainties," Agricultural Water Management, Elsevier, vol. 98(10), pages 1577-1588, August.
    16. Maciej Rysz & Mohammad Mirghorbani & Pavlo Krokhmal & Eduardo L. Pasiliao, 2014. "On risk-averse maximum weighted subgraph problems," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 167-185, July.
    17. Alonso-Ayuso, Antonio & Escudero, Laureano F. & Guignard, Monique & Weintraub, Andres, 2018. "Risk management for forestry planning under uncertainty in demand and prices," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1051-1074.
    18. Sun, Qi & Dong, Yucheng & Xu, Weidong, 2013. "Effects of higher order moments on the newsvendor problem," International Journal of Production Economics, Elsevier, vol. 146(1), pages 167-177.
    19. 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.
    20. Chenglong Zhang & Qiong Yue & Ping Guo, 2019. "A Nonlinear Inexact Two-Stage Management Model for Agricultural Water Allocation under Uncertainty Based on the Heihe River Water Diversion Plan," IJERPH, MDPI, vol. 16(11), pages 1-18, May.

    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:216:y:2012:i:1:p:214-224. 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.