Abstract
It is fair to say that recourse models are the most important class of models in stochastic programming, both in theory and in applications. Recourse models are reformulations of decision problems that model stochastic infeasibilities by means of corrections afterwards. The penalty costs of such corrections are included in the objective function. After an introduction of such recourse actions in deterministic LP, this chapter discusses the basics of recourse models in stochastic linear programming: representations, modeling, properties, and algorithms.
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Klein Haneveld, W.K., van der Vlerk, M.H., Romeijnders, W. (2020). Recourse Models. In: Stochastic Programming. Graduate Texts in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-29219-5_3
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DOI: https://doi.org/10.1007/978-3-030-29219-5_3
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