IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/46291.html
   My bibliography  Save this paper

Data-driven chance-constrained optimization based on Gaussian Mixture Models

Author

Listed:
  • Corredera Barbado, Alberto
  • Ruiz Mora, Carlos
  • Prieto Fernández, Francisco Javier

Abstract

In this work, we present a novel approach based on the combination of Gaussian Mixture Models (GMM) and Chance-Constrained Optimization (CCO). The method proposed deals with the often difficult task of deriving exact estimates of the individual constraint quantiles in the presence of uncertainty for some or all of the optimization problem parameters. Although COO solution methods have been extensively studied when uncertainty is assumed to be normally distributed, only approximate solutions are considered when the uncertainty is not normal. We propose a reformulation of the COO problem that provides exact and tractable solutions. The performance of the method has been studied under different GMM parameterizations in simulated and real-world applications.

Suggested Citation

  • Corredera Barbado, Alberto & Ruiz Mora, Carlos & Prieto Fernández, Francisco Javier, 2025. "Data-driven chance-constrained optimization based on Gaussian Mixture Models," DES - Working Papers. Statistics and Econometrics. WS 46291, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:46291
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/70c2819b-f0aa-45b5-b37f-007787bb89f6/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    2. Owen, Joel & Rabinovitch, Ramon, 1983. "On the Class of Elliptical Distributions and Their Applications to the Theory of Portfolio Choice," Journal of Finance, American Finance Association, vol. 38(3), pages 745-752, June.
    3. Wenqing Chen & Melvyn Sim, 2009. "Goal-Driven Optimization," Operations Research, INFORMS, vol. 57(2), pages 342-357, April.
    4. B. K. Pagnoncelli & S. Ahmed & A. Shapiro, 2009. "Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications," Journal of Optimization Theory and Applications, Springer, vol. 142(2), pages 399-416, August.
    5. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    6. Makio Ishiguro & Yosiyuki Sakamoto & Genshiro Kitagawa, 1997. "Bootstrapping Log Likelihood and EIC, an Extension of AIC," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(3), pages 411-434, September.
    7. Dimitris Bertsimas & Melvyn Sim & Meilin Zhang, 2019. "Adaptive Distributionally Robust Optimization," Management Science, INFORMS, vol. 65(2), pages 604-618, February.
    8. Grani A. Hanasusanto & Vladimir Roitch & Daniel Kuhn & Wolfram Wiesemann, 2017. "Ambiguous Joint Chance Constraints Under Mean and Dispersion Information," Operations Research, INFORMS, vol. 65(3), pages 751-767, June.
    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. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    2. Wang, Fan & Zhang, Chao & Zhang, Hui & Xu, Liang, 2021. "Short-term physician rescheduling model with feature-driven demand for mental disorders outpatients," Omega, Elsevier, vol. 105(C).
    3. van Eekelen, Wouter, 2023. "Distributionally robust views on queues and related stochastic models," Other publications TiSEM 9b99fc05-9d68-48eb-ae8c-9, Tilburg University, School of Economics and Management.
    4. Haolin Ruan & Zhi Chen & Chin Pang Ho, 2023. "Adjustable Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1002-1023, September.
    5. Liu, Kanglin & Li, Qiaofeng & Zhang, Zhi-Hai, 2019. "Distributionally robust optimization of an emergency medical service station location and sizing problem with joint chance constraints," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 79-101.
    6. Zhi Chen & Peng Xiong, 2023. "RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 717-724, July.
    7. Feng Liu & Zhi Chen & Shuming Wang, 2023. "Globalized Distributionally Robust Counterpart," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1120-1142, September.
    8. Algo Carè & Simone Garatti & Marco C. Campi, 2019. "The wait-and-judge scenario approach applied to antenna array design," Computational Management Science, Springer, vol. 16(3), pages 481-499, July.
    9. Zhi Chen & Weijun Xie, 2021. "Sharing the value‐at‐risk under distributional ambiguity," Mathematical Finance, Wiley Blackwell, vol. 31(1), pages 531-559, January.
    10. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.
    11. Antonio J. Conejo & Nicholas G. Hall & Daniel Zhuoyu Long & Runhao Zhang, 2021. "Robust Capacity Planning for Project Management," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1533-1550, October.
    12. Jiang, Jie & Peng, Shen, 2024. "Mathematical programs with distributionally robust chance constraints: Statistical robustness, discretization and reformulation," European Journal of Operational Research, Elsevier, vol. 313(2), pages 616-627.
    13. Xie, Chen & Wang, Liangquan & Yang, Chaolin, 2021. "Robust inventory management with multiple supply sources," European Journal of Operational Research, Elsevier, vol. 295(2), pages 463-474.
    14. Siqin, Zhuoya & Niu, DongXiao & Li, MingYu & Gao, Tian & Lu, Yifan & Xu, Xiaomin, 2022. "Distributionally robust dispatching of multi-community integrated energy system considering energy sharing and profit allocation," Applied Energy, Elsevier, vol. 321(C).
    15. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    16. Roos, Ernst & den Hertog, Dick, 2019. "Reducing conservatism in robust optimization," Other publications TiSEM ad0238cd-de7a-4366-b487-b, Tilburg University, School of Economics and Management.
    17. Yun Fong Lim & Chen Wang, 2017. "Inventory Management Based on Target-Oriented Robust Optimization," Management Science, INFORMS, vol. 63(12), pages 4409-4427, December.
    18. Shiting Cui & Ruijin Zhu & Yao Gao, 2022. "Distributionally Robust Optimization of an Integrated Energy System Cluster Considering the Oxygen Supply Demand and Multi-Energy Sharing," Energies, MDPI, vol. 15(22), pages 1-24, November.
    19. Algo Carè & Simone Garatti & Marco C. Campi, 2014. "FAST---Fast Algorithm for the Scenario Technique," Operations Research, INFORMS, vol. 62(3), pages 662-671, June.
    20. Guo, Xiaotong & Caros, Nicholas S. & Zhao, Jinhua, 2021. "Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 161-189.

    More about this item

    Keywords

    OR in energy;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cte:wsrepe:46291. 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: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    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.