This dissertation studies a framework in support biomass wood pellet supply chain. The worldwide wood pellet market is growing at a phenomenal rate. However, the economic sustainment of this business depends on how well the producers manage the uncertainty associated with biomass yield and quality. In the first part of the dissertation, we propose a two-stage stochastic programming model that optimizes different critical decisions (e.g., harvesting, storage, transportation, quality inspection, and production decisions) of a biomass-to-pellet supply system under biomass yield and quality uncertainty to economically produce pellets while accounting for the different pellet standards set forward by the U.S. and European markets. The study develops a hybrid algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging algorithm. We propose two parallelization schemes to efficiently speed up the convergence of the overall algorithm. We use Mississippi as a testing ground to visualize and validate the algorithms performance. Experimental results indicate that the biomass-to-pellet supply system is sensitive to the biomass quality parameters (e.g., ash and moisture contents). In the second part of the dissertation, we propose a bi-level mixed-integer linear programming model that captures important features such as the hurricane’s degree, quality of damaged timbers, price-related issues, optimizes different critical decisions (e.g., purchasing, storage, and transportation decisions) of a post-hurricane damaged timber management problem. Lack of efficient tools to manage the wood market interactions in the post-hurricane situation increases timber salvage loss drastically. The overall goal is to provide an efficient decision-making tool for planning and recovering damaged timber to maximize its monetary value and mitigate its negative ecological impacts. Due to the complexity associated with solving the proposed model, we developed two exact solution methods, namely, the enhanced Benders decomposition and the Benders-based branch-and-cut algorithms, to efficiently solve the model in a reasonable time-frame. We use 15 coastal counties in southeast Mississippi to visualize and validate the algorithms' performance. Key managerial insights are drawn on the sensitivity of a number of critical parameters, such as selling/purchasing prices offered by the landowners/mills, quality-level, and deterioration rate of the damaged timbers on their economic recovery following a natural catastrophe.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6181 |
| Date | 06 August 2021 |
| Creators | Aladwan, Badr S |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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