Tractable continuous approximations for constraint selection via cardinality minimization
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Harsha Gangammanavar
Stochastic Decomposition Method for Two-Stage Distributionally Robust Optimization
In this paper, we present a sequential sampling-based algorithm for the two-stage distributionally robust linear programming (2-DRLP) models. The 2-DRLP models are defined over a general class of ambiguity sets with discrete or continuous probability distributions. The algorithm is a distributionally robust version of the well-known stochastic decomposition algorithm of Higle and Sen (Math. of … Read more
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Driven by ambitious renewable portfolio standards, variable energy resources (such as wind and solar) are expected to impose unprecedented levels of uncertainty to power system operations. The current practice of planning operations with deterministic optimization tools may be ill-suited for a future where uncertainty is abundant. To overcome the reliability challenges associated with the large-scale … Read more
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Stochastic Decomposition for Two-stage Stochastic Linear Programs with Random Cost Coefficients
Stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is designed to discover an appropriate sample size for a given SP instance, thus precluding the need for either scenario reduction or arbitrary sample sizes to create sample average … Read more
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We present a two-stage stochastic linear programming model to manage the inventory of vaccine vials in developing countries. In these countries immunization programs often involve targeted outreach services in remote locations. Organizations managing these programs are in need of tools which identify inventory replenishment and vaccine utilization plans to minimize costs and wastage while achieving … Read more