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Optimization Under Uncertainty

In: Introduction to Applied Optimization

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  • Urmila M. Diwekar

    (Vishwamitra Research Institute)

Abstract

In previous chapters, we looked at various optimization problems. Depending on the decision variables, objectives, and constraints, the problems were classified as LPLP , NLP NLP , IP IP , MILP MILP , or MINLP MINLP . However, as stated above, the future cannot be perfectly forecast but instead should be considered random random or uncertain. Optimization under uncertainty refers to this branch of optimization where there are uncertainties involved in the data or the model, and is popularly known as stochastic programming stochastic programming or stochastic optimization stochastic optimization problems.

Suggested Citation

  • Urmila M. Diwekar, 2020. "Optimization Under Uncertainty," Springer Optimization and Its Applications, in: Introduction to Applied Optimization, edition 3, chapter 0, pages 151-215, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-55404-0_5
    DOI: 10.1007/978-3-030-55404-0_5
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    Cited by:

    1. Tao Zhang & Minli Wang & Peihong Wang & Junyu Liang, 2020. "Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty," Energies, MDPI, vol. 13(14), pages 1-17, July.
    2. Alaric Christian Montenon & Costas Papanicolas, 2020. "Economic Assessment of a PV Hybridized Linear Fresnel Collector Supplying Air Conditioning and Electricity for Buildings," Energies, MDPI, vol. 14(1), pages 1-25, December.
    3. Daniel Akinyele & Abraham Amole & Elijah Olabode & Ayobami Olusesi & Titus Ajewole, 2021. "Simulation and Analysis Approaches to Microgrid Systems Design: Emerging Trends and Sustainability Framework Application," Sustainability, MDPI, vol. 13(20), pages 1-26, October.
    4. Paweł Węgierek & Justyna Pastuszak & Kamil Dziadosz & Marcin Turek, 2020. "Influence of Substrate Type and Dose of Implanted Ions on the Electrical Parameters of Silicon in Terms of Improving the Efficiency of Photovoltaic Cells," Energies, MDPI, vol. 13(24), pages 1-17, December.
    5. Doan, Xuan Vinh, 2022. "Distributionally robust optimization under endogenous uncertainty with an application in retrofitting planning," European Journal of Operational Research, Elsevier, vol. 300(1), pages 73-84.

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