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Ellis, K. ; Ferrell, W. ; Knapp, J. (Ed.)The mass transportation distance rank histogram (MTDRh) was developed to assess the reliability of any given scenario generation process for a two-stage, risk-neutral stochastic program. Reliability is defined loosely as goodness of fit between the generated scenario sets and corresponding observed values over a collection of historical instances. This graphical tool can diagnose over- or under-dispersion and/or bias in the scenario sets and support hypothesis testing of scenario reliability. If the risk-averse objective is instead to minimize CVaR of cost, the only important, or effective, scenarios are those that produce cost in the upper tail of the distribution at the optimal solution. We describe a procedure to adapt the MTDRh for use in assessing the reliability of scenarios relative to the upper tail of the cost distribution. This adaptation relies on a conditional probability distribution derived in the context of assessing the effectiveness of scenarios. For a risk-averse newsvendor formulation, we conduct simulation studies to systematically explore the ability of the CVaR-adapted MTDRh to diagnose different ways that scenario sets may fail to capture the upper tail of the cost distribution near optimality. We conjecture that, as with the MTDRh and its predecessor minimum spanning tree rank histogram, the naturemore »
NRT-INFEWS: The DataFEWSion Traineeship Program for Innovations at the Nexus of Food Production, Renewable Energy, and Water QualitySustainable provision of food, energy and clean water requires understanding of the interdependencies among systems as well as the motivations and incentives of farmers and rural policy makers. Agriculture lies at the heart of interactions among food, energy and water systems. It is an increasingly energy intensive enterprise, but is also a growing source of energy. Agriculture places large demands on water supplies while poor practices can degrade water quality. Each of these interactions creates opportunities for modeling driven by sensor-based and qualitative data collection to improve the effectiveness of system operation and control in the short term as well as investments and planning for the long term. The large volume and complexity of the data collected creates challenges for decision support and stakeholder communication. The DataFEWSion National Research Traineeship program aims to build a community of researchers that explores, develops and implements effective data-driven decision-making to efficiently produce food, transform primary energy sources into energy carriers, and enhance water quality. The initial cohort includes PhD students in agricultural and biosystems, chemical, and industrial engineering as well as statistics and crop production and physiology. The project aims to prepare trainees for multiple career paths such as research scientist, bioeconomy entrepreneur,more »