Jacqueline A. Isaacs
James C. Benneyan, Ahmed Busnaina, Michael J. Ellenbecker
Date of Award
Doctor of Philosophy
Department or Academic Unit
College of Engineering. Department of Mechanical and Industrial Engineering.
desirability functions, Monte Carlo simulation, multi-criteria optimization, nanotechnology, uncertainty
Engineering | Industrial Engineering
As nanotechnology moves from development to commercialization, interest has grown in understanding economic and environmental trade-offs regarding nanomanufacturing. Nanotechnology holds enormous promise in energy, electronics, healthcare, consumer products, and other applications. In parallel, significant concerns exist regarding possible environmental, health, and safety (EHS) risks of engineered nanomaterials and nanomanufacturing processes, and in turn, there is significant uncertainty about appropriate workplace safeguards, commercialization regulations, and future market conditions. Given these uncertainties, methods for informing nanotechnology manufacturing and commercialization decisions are increasingly important.
This study assesses the utility of several mathematical modeling techniques in addressing uncertainty across a range of nanotechnology research, production, and development contexts. Although the number of published studies has increased significantly over the past 10 years, very little practical guidance in managing engineered nanomaterials and nanomanufacturing processes is currently available for researchers, businesses, and policy makers. Four modeling methods - Monte Carlo, multi-criteria decision making, stochastic programming, and desirability function models - are reviewed in detail to illustrate how they can guide decision makers to develop a better understanding of uncertainties and trade-offs regarding nanotechnology development. This mathematical modeling study is then extended to develop decision support models, based on Monte Carlo risk modeling and desirability functions, for nanomanufacturing case studies.
Risk analysis Monte Carlo models are developed to assess production cost and exposure trade-offs of the high pressure carbon monoxide (HiPco) single-walled carbon nanotube manufacturing process. Assumptions regarding the timing, frequency, magnitude, and expense of EHS standards are modeled as stochastic events and examined for their impact on the expected values, variances, and probability distributions of total production costs and occupational exposure. Desirability function models are developed to optimize nanomanufacturing production scale-up decisions, which often involve multiple conflicting production, cost, and safety criteria. These planning models tend to be more intuitive for practitioners than some other multi-criteria decision making methods. Sensitivity and what-if analyses are conducted to further study the impact of uncertainties on the optimal solutions. These models provide important insights to allow informed decisions in nanotechnology manufacturing and commercialization given the significant uncertainty regarding potential nanomaterial risks, future regulatory environments, production capability, and all associated costs.
Zeynep Damla Ok
Ok, Zeynep Damla, "Risk management optimization models for nanomanufacturing" (2010). Industrial Engineering Dissertations. Paper 8. http://hdl.handle.net/2047/d20002810
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