Abstract
Nanotechnology companies face uncertainties from several fronts including occupational safety regulation, demand for nanoproducts, and technology advancements. These uncertainties can prove to be a challenge for planning the production capacity expansion of engineered nanomaterials or nanoenabled products. Exploratory Monte Carlo simulation results indicate that these uncertainties have a significant effect on potential revenue and that there are opportunities for making optimal sustainable capacity expansion decisions by evaluating all possible future scenarios through optimization models. Accordingly, this work develops a multistage stochastic programming (MSP) model to determine the optimal timing of expansion, expansion size, process type, production volume, and also the occupational safety controls in the company to ultimately minimize the total production cost. This MSP model also helps decision makers to achieve sustainable manufacturing goals by reducing the unnecessary capacity expansion and occupational exposure.
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