Integrated supply chain design for sustainable and resilient development of biofuel production

The U.S. biofuel industry has been experiencing phenomenal growth during the last decade, which may be partially attributed to the Energy Policy Act of 2005 and the Energy Independence and Security Act of 2007. With such a sharp increase in biofuel demand, ethanol manufacturing infrastructure must be significantly expanded. The booming industry can have profound impacts on the economy, environment and society at national, regional and local levels.;The economic feasibility and environmental sustainability of biofuel industry will be highly dependent on the strategic design of the biomass-to-biofuel supply chain. Many factors play important roles in the optimal design of a biofuel supply chain, such as the regional geographical features, economic structure, spatial distribution of demand, and transportation infrastructure (network, modes and cost). They are also interdependent and in uenced by the configuration of a biofuel supply chain.;We first review state of the art studies on biofuel development and its social economic impacts, with a focus on transportation infrastructure, food versus fuel debate and farmland use. We also discuss the competition between biofuel supply chain and the existing food supply chain, as well as possible business scenarios between farmers and biofuel manufacturers.;The technical part of this dissertation starts with an integrated biofuel supply chain model in which the shipment routing of both biomass feedstock and fuel product and the resulting traffic congestion impact are incorporated to decide optimal locations of biofuel refineries. A Lagrangian relaxation based heuristic algorithm is introduced to obtain nearoptimum feasible solutions efficiently. To further improve optimality, a branch-and-bound framework (with linear programming relaxation and Lagrangian relaxation bounding procedures) is developed. Numerical experiments are conducted to demonstrate that the proposed algorithms solve the problem effectively. An empirical Illinois case study and a series of sensitivity analyses are conducted to show the effects of highway congestion on refinery location design and total system costs. We then build on this work to develop a more complex model by considering highway pavement rehabilitation decisions under pavement deterioration and traffic user equilibrium with congestion in highway transportation networks.;We extended our models to account for uncertainties and risks in biofuel supply chain design. We proposed a stochastic version of our supply chain design model that deals with feedstock supply and ethanol demand uncertainties. From this model, the optimal supply chain configuration should well balance the trade-off between the expected operational efficiency under uncertainties and the capital investment cost for building refineries. Monte Carlo method is adopted to approximate the probabilistic distribution of spatial dependent supply and demand and expected total system cost. Besides the feedstock supply and ethanol demand uncertainties, bio-ethanol facilities and infrastructure are also susceptible to disruption hazards. We further applies discrete and continuous reliable facility location models to the design of reliable bio-ethanol supply chains for the State of Illinois. The impacts of both site independent and dependent disruptions are analyzed with a series of numerical experiments. Sensitivity analysis is also conducted to show how refinery failure probabilities and penalty cost (for ethanol production reduction) affect optimal supply chain configuration and the total expected system cost.;Another major issue that we address is the allocation of farmland between food and fuel productions. To this end, we develop game-theoretic models to find the optimal design of a biofuel supply chain under farmers' land use choice and feedstock market equilibrium, and draw insights on different possible business partnerships between the biofuel industry and farmers. To solve the game theoretic models, we develop a solution approach that transforms the original discrete mathematical program with equilibrium constraints (DC-MPEC) into to a solvable mixed integer quadratic programming (MIQP) problem. In the last chapter, we further build on this work to analyze how possible governmental regulations/policies on agricultural land use and greenhouse gas (GHG) emission would affect the optimal biofuel supply chain design. We also develop two iterative relaxation algorithms to solve larger scale DC-MPEC problem instances more effectively and efficiently.;In this work, a range of analytical approaches and customized solution algorithms (such as Lagrangian relaxation, linear relaxation, branch and bound, reformulation, penalty method, quasi-probabilistic method, and Monte-Carlo method) are developed to solve large-scale instances of these models efficiently. The proposed models and solution algorithms are tested in various empirical case studies, and the results not only provide insights on the potential economic, social, and environmental impacts of the biofuel industry, but also provide guidelines for its sustainable development. The methodology framework can also be applied to transportation planning and supply chain design problems in many other contexts.