Title: Weighted Maximum Multicommodity Flows over timeSpeaker: Haripriya Pulyassary Affiliation: University of Waterloo Zoom: Contact Sharat Ibrahimpur
In various applications, flow does not travel instantaneously through a network, and the amount of flow traveling on an edge may vary over time. This temporal dimension is not captured by the classic static network flow models but can be modeled using flows over time.
Title: Data-Driven Sample-Average Approximation for Stochastic Optimization with Covariate InformationSpeaker: Jim Luedtke Affiliation: University of Wisconsin-Madison Zoom: Please email Emma Watson
We consider optimization models for decision-making in which parameters within the optimization model are uncertain, but predictions of these parameters can be made using available covariate information. We consider a data-driven setting in which we have observations of the uncertain parameters together with concurrently-observed covariates. Given a new covariate observation, the goal is to choose a decision that minimizes the expected cost conditioned on this observation. We investigate a data-driven framework in which the outputs from a machine learning prediction model are directly used to define a stochastic programming sample average approximation (SAA).