Stochastic programming

In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but follow known probability distributions. This framework contrasts with deterministic optimization, in which all problem parameters are assumed to be known exactly. The goal of stochastic programming is to find a decision which both optimizes some criteria chosen by the decision maker, and appropriately accounts for the uncertainty of the problem parameters. Because many real-world decisions involve uncertainty, stochastic programming has found applications in a broad range of areas ranging from finance to transportation to energy optimization. Two-stage problems The basic idea of two-stage stochastic programming is that (optimal) decisions should be based on data available at the time the decisions are made and cannot depend on f

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Related publications (62)

Scarcity Mitigation and Risk-Averse Clearing Model in the Swiss Reserve Market

Farzaneh Abbaspourtorbati

This paper presents a real-world application of stochastic programming in the Swiss hydro-based system. The seasonal hydrological conditions may cause insufficient market volume to an extent that security criteria (i.e., the criteria determining of reserve amount) may not be met. In this situations, the question arises to what extend the current pre-defined security criteria can be relaxed while respecting the current market properties. We provide a framework accounting for the trade-off between the expected procurement cost of reserves and security criteria. Additionally, this paper provides a risk-averse formulation of the current clearing model. The proposed approach is simulated using data of the Swiss reserve market to illustrate technical and economic aspects.

Ieee2016

Distributionally Robust Optimization with Markovian Data

Daniel Kuhn, Mengmeng Li, Tobias Sutter

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven distributionally robust optimization model to estimate the problem’s objective function and optimal solution. By leveraging results from large deviations theory, we derive statistical guarantees on the quality of these estimators. The underlying worst-case expectation problem is nonconvex and involves O(d^2) decision variables. Thus, it cannot be solved efficiently for large d. By exploiting the structure of this prob- lem, we devise a customized Frank-Wolfe algorithm with convex direction-finding subproblems of size O(d). We prove that this algorithm finds a stationary point efficiently under mild conditions. The efficiency of the method is predicated on a dimensionality reduction enabled by a dual reformulation. Numerical experiments indicate that our approach has better computational and statistical properties than the state-of-the-art methods.

2021

"Dice"-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?

Daniel Kuhn, Wolfram Wiesemann

Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adverse distribution in an ambiguity set. We investigate under which circumstances such deterministic decisions are strictly outperformed by random decisions which depend on a randomization device producing uniformly distributed samples that are independent of all uncertain factors affecting the decision problem. We find that in the absence of distributional ambiguity, deterministic decisions are optimal if both the risk measure and the feasible region are convex, or alternatively if the risk measure is mixture-quasiconcave. We show that several classes of risk measures, such as mean (semi-)deviation and mean (semi-)moment measures, fail to be mixture-quasiconcave and can therefore give rise to problems in which the decision maker benefits from randomization. Under distributional ambiguity, on the other hand, we show that for any ambiguity averse risk measure there always exists a decision problem (with a nonconvex—e.g., mixed-integer—feasible region) in which a randomized decision strictly dominates all deterministic decisions.

2019

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