Optimization Under Uncertainty

Urmila M. Diwekar
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Urmila M. Diwekar: Vishwamitra Research Institute

Chapter Chapter 5 in Introduction to Applied Optimization, 2020, pp 151-215 from Springer

Abstract: 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.

Date: 2020
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DOI: 10.1007/978-3-030-55404-0_5

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