Abstract
Earlier chapters dealt with problems arising from data uncertainty by examining the sensitivity of the model’s recommendations with respect to changes in the data. This chapter presents prescriptive approaches to problems of sensitivity analysis and parametric programming. It provides an introduction to stochastic programming and robust optimization models. Such models deal, in a constructive manner, with noisy, incomplete or uncertain data. Information about possible values of the problem data is incorporated in the model, and the model generates solutions that are less sensitive to data uncertainty. Stochastic linear programming and robust optimization models are introduced and applications are presented, with emphasis on financial planning problems.KeywordsStochastic ProgramRobust OptimizationPortfolio ManagementStochastic NetworkTerminal WealthThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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