Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Industrial Engineering

Committee Member

Dr. Sandra D. Ekşioğlu, Committee Chair

Committee Member

Dr. Amin Khademi

Committee Member

Dr. Michael Carbajales-Dale

Committee Member

Dr. B. Rae Cho


This dissertation presents optimization models to aid with the sustainable design and management of biopower (biomass cofiring) supply chains. We address three main challenges associated with today’s biopower projects: i) high cost of biomass collection, storage and delivery, ii) inefficiency of the mechanisms used to incentivize biomass usage for generating electricity, and iii) lack of clear understanding about the trade-offs between economic and environmental impacts of biopower supply chains. In order to address the high cost of delivering biomass, we present a novel mixed integer nonlinear program that integrates production and transportation decisions at power plants. Proposed model captures the loss in process efficiencies from using biomass, in-vestment and operational costs associated with cofiring, and savings due to production tax credit (PTC), a major governmental incentive to support biopower. We develop a La-grangian relaxation approach to provide upper bounds, and two linear approximations to provide lower bounds for the problem. An important finding is that the one-size-fits-all approach of PTC is not effective in motivating plants to utilize biomass and there is a need for sophisticated incentive schemes. In order to address the second issue, we propose alter-natives for the existing PTC incentive. The proposed flexible alternatives are functions of plant capacity and biomass cofiring ratio. We use a resource allocation framework to model and analyze the profit-earning potentials and fairness of the proposed incentive schemes. Finally, in order to address the last challenge, we propose a stochastic biobjective optimiza-tion model to analyze the economic and environmental impacts of biopower supply chains. The economic objective function maximizes the potential profits in the supply chain and the environmental objective function minimizes the life cycle greenhouse gasses (GHG). We use a life cycle assessment (LCA) approach to derive the emission factors for this objective function. We capture uncertainties of biomass quality and supply via the use of chance constraints. The results of this dissertation work are useful for electric utility companies and policy makers. Utility companies can use the proposed models to identify ways to improve biopower production, have better environmental performance, and make use of the existing incentives. Policy makers would gain insights on designing incentive schemes for a more efficient utilization of biomass and a fairer distribution of tax-payers money.