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Constraint generation for risk averse two-stage stochastic programs

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  • Mínguez, R.
  • van Ackooij, W.
  • García-Bertrand, R.

Abstract

A significant share of stochastic optimization problems in practice can be cast as two-stage stochastic programs. If uncertainty is available through a finite set of scenarios, which frequently occurs, and we are interested in accounting for risk aversion, the expectation in the recourse cost can be replaced with a worst-case function (i.e., robust optimization) or another risk-functional, such as conditional value-at-risk. In this paper we are interested in the latter situation especially when the number of scenarios is large. For computational efficiency we suggest a (clustering and) constraint generation algorithm. We establish convergence of these two algorithms and demonstrate their effectiveness through various numerical experiments.

Suggested Citation

  • Mínguez, R. & van Ackooij, W. & García-Bertrand, R., 2021. "Constraint generation for risk averse two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 288(1), pages 194-206.
    • Handle: RePEc:eee:ejores:v:288:y:2021:i:1:p:194-206
    DOI: 10.1016/j.ejor.2020.05.064
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    References listed on IDEAS

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    3. Díaz Cachinero, Pablo & Gázquez Torres, Ricardo & Mínguez Solana, Roberto, 2026. "Solving Stochastic Two-Stage Programs with Fixed Recourse: The Dual-Clustering Constraint-Generation Approach," DES - Working Papers. Statistics and Econometrics. WS 49113, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Zhao, Shuaiqi & Yang, Hualong & Zheng, Jianfeng & Li, Dechang, 2024. "A two-step approach for deploying heterogeneous vessels and designing reliable schedule in liner shipping services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
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