Stochastic Programming: Optimization When Uncertainty Matters

Julia L. Higle

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Risk and Portfolio Optimization Electric Power System Optimization Advanced Optimization Algorithms Research

Abstract

Abstract Stochastic Programming (SP) was first introduced by George Dantzig in the 1950’s. Since that time, tremendous progress toward an understanding of properties of SP models and the design of algorithmic approaches for solving them has been made. As a result, SP is gaining recognition as a viable approach for large scale models of decisions under uncertainty. In this paper, we present an introduction to stochastic programming models and methodology at a level that is intended to be accessible to the breadth of members within the INFORMS community.

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References

2000Optimization of conditional value-at-risk 2002Conditional value-at-risk for general loss distributions 1959Chance-Constrained Programming 2010Stochastic Programming 2002The Sample Average Approximation Method for Stochastic Discrete Optimization 1995Stochastic Programming 1991Scenarios and Policy Aggregation in Optimization Under Uncertainty 2004Linear Programming Under Uncertainty

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