Optimization approaches for the economic and environmental analysis of biomass, biofuel, and food production

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Issue Date
2015-05
Authors
Cobuloglu, Halil I.
Advisor
Buyuktahtakin, Esra
Citation
Abstract

This dissertation examines optimization approaches to biomass and food production (BFP) at the farm level. The goal of this study is to analyze the environmental and economic effects of utilizing different crop types under various management scenarios. In this study, first we provide a unique optimization approach of quantifying and formulating the economic and environmental benefits of switchgrass production at the farm level. In particular, we propose a multi-objective mixed-integer programming (MIP) model that maximizes the revenue from harvested switchgrass biomass and the economic value obtained from positive environmental impacts of switchgrass yield during the planning horizon. Second, we investigate the economic and environmental tradeoffs between biofuel and food production from switchgrass and corn. This model maximizes the total profit of farmers while ensuring a sustainable food supply. Then, we develop a stochastic multi-criteria decision-making tool for decision makers (DMs) and farmers to select the most sustainable crop type in biomass production. In this method, literature and expert opinions are utilized in order to build up the evaluation criteria with respect to economic, environmental, and social aspects. Finally, we expand the optimization of food and biomass production with a stochastic modelling approach. We perform a decomposition algorithm in order to increase solution speed and solution quality of the stochastic model. The developed mathematical models provide optimal decisions regarding land allocation to food and energy crops; time, amount, and location of seeding, harvesting, and transportation; and budget allocations to farm operations. We apply the proposed methods to various cases in order to evaluate BFP in Kansas.

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Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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