A key element for realizing long term sustainable use of any metal will be a robust secondary recovery industry. Secondary recovery forestalls depletion of non-renewable resources and avoids the deleterious effects of extraction and winning (albeit by substituting some effects of its own). For most metals, the latter provides strong motivation for recycling; for light metals, like aluminum, the motivation is compelling. Along aluminum's life-cycle there are a variety of leverage points for increasing the usage of secondary or recycled materials. This thesis aims to improve materials decision making in two of these key areas: 1) blending decisions in manufacturing, and 2) alloy design decisions in product development. The usage of recycled aluminum in alloy blends is greatly hindered by variation in the raw material composition. Currently, to accommodate compositional variation, firms commonly set production targets well inside the window of compositional specification required for performance reasons. Window narrowing, while effective, does not make use of statistical sampling data, leading to sub-optimal usage of recycled materials. This work explores the use of stochastic programming techniques which allow explicit consideration of statistical information on composition. The computational complexity of several methods is quantified in order to select a single method for comparison to deterministic models, in this case, a chance-constrained model was optimal. The framework and a case study of cast and wrought production with available scrap materials are presented.
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