Bio-energy Logistics Network Design Under Price-based Supply and Yield Uncertainty

In this dissertation, we study the design and planning of bio-energy supply chain networks. This dissertation consists of 3 studies that focus on different aspects of bio-energy supply chain systems.

In the first study, we consider planning and design of an extended supply chain for bio-energy networks in an integrated fashion while simultaneously addressing strategic and tactical decisions pertaining to location, production, inventory, and distribution in a multi-period planning horizon setting. For an efficient solution of our model, we suggest a Benders Decomposition based algorithm that can handle realistic size problems for design and analysis purposes. We provide computational results that demonstrate the efficiency of the solution approach on a wide ranging set of problem instances. Furthermore, we develop a realistic case by utilizing data pertaining to the state of Texas and conduct an extensive analysis on the effects of varying input parameters on the design outcomes for a bio-energy supply chain network.

In the second study, we consider a two-stage stochastic problem to model farm-to-biorefinery biomass logistics while designing a policy that encourages farmers to plant biomass energy crops by offering them a unit wholesale price. In the first-stage, the model determines the supply chain network structure as well as the policy parameter, which is the biomass wholesale price offered to farmers. Second-stage problem is to determine the logistical decisions such as transportation, salvaging and out-sourcing. To solve this problem, we propose a solution framework that uses an algorithm based on the L-shaped method along with a Sample Average Approximation (SAA) approach. An extensive case study by varying some of the problem input parameters is conducted in Texas and the effects on the policy parameter (wholesale price), supply chain network design and expected total system cost are observed.

In the last study, we propose a two-stage stochastic program to model a multi-period biomass-biofuel supply chain system to maximize the expected total system profit. We utilize a similar policy used in the second study to stimulate biomass energy crop production. Our model determines the policy parameter and the supply chain network structure in the first-stage and the tactical decisions for every time period in the second-stage. To solve this problem efficiently, we propose a solution algorithm based on the L-shaped method. Moreover, we also employ SAA approach in our solution methodology to statistically justify our solution quality. A case study is conducted in Texas for different biofuel prices and we analyze changes in the expected system profit the policy parameter and the supply chain network structure. Our case study results indicate that biofuel price needs to be at least $2.62/gal for the system to have a profit.