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On capacity expansion planning under strategic and operational uncertainties based on stochastic dominance risk averse management

Author

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  • Laureano F. Escudero

    (Universidad Rey Juan Carlos, URJC)

  • Juan F. Monge

    (Universidad Miguel Hernández, UMH)

Abstract

A new scheme for dealing with uncertainty in scenario trees is presented for dynamic mixed 0–1 optimization problems with strategic and operational stochastic parameters. Let us generically name this type of problems as capacity expansion planning (CEP) in a given system, e.g., supply chain, production, rapid transit network, energy generation and transmission network, etc. The strategic scenario tree is usually a multistage one, and the replicas of the strategic nodes root structures in the form of either a special scenario graph or a two-stage scenario tree, depending on the type of operational activity in the system. Those operational scenario structures impact in the constraints of the model and, thus, in the decomposition methodology for solving usually large-scale problems. This work presents the modeling framework for some of the risk neutral and risk averse measures to consider for CEP problem solving. Two types of risk averse measures are considered. The first one is a time-inconsistent mixture of the chance-constrained and second-order stochastic dominance (SSD) functionals of the value of a given set of functions up to the strategic nodes in selected stages along the time horizon, The second type is a strategic node-based time-consistent SSD functional for the set of operational scenarios in the strategic nodes at selected stages. A specialization of the nested stochastic decomposition methodology for that problem solving is outlined. Its advantages and drawbacks as well as the framework for some schemes to, at least, partially avoid those drawbacks are also presented.

Suggested Citation

  • Laureano F. Escudero & Juan F. Monge, 2018. "On capacity expansion planning under strategic and operational uncertainties based on stochastic dominance risk averse management," Computational Management Science, Springer, vol. 15(3), pages 479-500, October.
    • Handle: RePEc:spr:comgts:v:15:y:2018:i:3:d:10.1007_s10287-018-0318-9
    DOI: 10.1007/s10287-018-0318-9
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    References listed on IDEAS

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    Cited by:

    1. Castro, Jordi & Escudero, Laureano F. & Monge, Juan F., 2023. "On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 268-285.
    2. Domínguez, Ruth & Vitali, Sebastiano & Carrión, Miguel & Moriggia, Vittorio, 2021. "Analysing decarbonizing strategies in the European power system applying stochastic dominance constraints," Energy Economics, Elsevier, vol. 101(C).
    3. Laureano F. Escudero & Juan F. Monge, 2021. "On Multistage Multiscale Stochastic Capacitated Multiple Allocation Hub Network Expansion Planning," Mathematics, MDPI, vol. 9(24), pages 1-39, December.
    4. Escudero, Laureano F. & Monge, Juan F. & Rodríguez-Chía, Antonio M., 2020. "On pricing-based equilibrium for network expansion planning. A multi-period bilevel approach under uncertainty," European Journal of Operational Research, Elsevier, vol. 287(1), pages 262-279.
    5. Escudero, Laureano F. & Garín, M. Araceli & Monge, Juan F. & Unzueta, Aitziber, 2020. "Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management," European Journal of Operational Research, Elsevier, vol. 285(3), pages 988-1001.
    6. Hongyu Zhang & Ignacio E. Grossmann & Asgeir Tomasgard, 2024. "Decomposition methods for multi-horizon stochastic programming," Computational Management Science, Springer, vol. 21(1), pages 1-24, June.
    7. Baptista, Susana & Barbosa-Póvoa, Ana Paula & Escudero, Laureano F. & Gomes, Maria Isabel & Pizarro, Celeste, 2019. "On risk management of a two-stage stochastic mixed 0–1 model for the closed-loop supply chain design problem," European Journal of Operational Research, Elsevier, vol. 274(1), pages 91-107.

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