22 citations · 43 across the 10 of their papers we have counts for
9 papers · 1 filter
Bounds for multi-horizon stochastic optimization with application to power generation and transmission expansion planning
Giovanni Micheli, V Varagapriya, Francesca Maggioni +1
This paper investigates computationally efficient methods for deriving bounds on the optimal value of multi-horizon stochastic optimization problems, with a particular focus on app…
Multi-horizon optimization for domestic renewable energy system design under uncertainty
Giovanni Micheli, Laureano F. Escudero, Francesca Maggioni +1
In this paper we address the challenge of designing optimal domestic renewable energy systems under multiple sources of uncertainty appearing at different time scales. Long-term un…
Residuals-Based Contextual Distributionally Robust Optimization with Decision-Dependent Uncertainty: Theoretical Guarantees and Decomposition Algorithm
Qing Zhu, Xian Yu, Guzin Bayraksan
We consider a residuals-based distributionally robust optimization (DRO) model, where the underlying uncertainty depends on both covariate information and our decisions. We adopt b…
Data-Driven Sample Average Approximation with Covariate Information
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
We study optimization for data-driven decision-making when we have observations of the uncertain parameters within the optimization model together with concurrent observations of c…
Effective Scenarios in Multistage Distributionally Robust Optimization with a Focus on Total Variation Distance
Hamed Rahimian, Guzin Bayraksan, Tito Homem-de-Mello
We study multistage distributionally robust optimization (DRO) to hedge against ambiguity in quantifying the underlying uncertainty of a problem. Recognizing that not all the reali…
Heteroscedasticity-aware residuals-based contextual stochastic optimization
Rohit Kannan, Güzin Bayraksan, James Luedtke
We explore generalizations of some integrated learning and optimization frameworks for data-driven contextual stochastic optimization that can adapt to heteroscedasticity. We ident…
Residuals-based distributionally robust optimization with covariate information
Rohit Kannan, Güzin Bayraksan, James R. Luedtke
We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertai…
A Multistage Distributionally Robust Optimization Approach to Water Allocation under Climate Uncertainty
Jangho Park, Guzin Bayraksan
This paper investigates a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of cond…
Variance Reduction for Sequential Sampling in Stochastic Programming
Jangho Park, Rebecca Stockbridge, Güzin Bayraksan
This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming an…