Congratulations to Tyler Wilson and Garrett Hairston for graduating with their MS degrees, they both defended their MS theses this May. Garrett started in the BiSSL group as an undergraduate researcher back in Fall 2019 and Tyler began his MS with BiSSL in Fall 2020.

Garrett’s thesis is titled: “Using Bio-Inspired Techniques to Design for Improved Sustainability and Robustness in Net Zero Communities”

In the global effort to combat climate change, the continued emergence of Net Zero Communities (NZCs) can play a large role in establishing a sustainable foundation on which progress can be made. NZC design, however, is complicated by the need to balance the system’s ability to achieve sustainable performance with its ability to maintain system operation during disturbances. These two design objectives, sustainable use of resources and system robustness, are often found in opposition to one another, but design inspiration can be taken from biological ecosystems, which have benefitted from generations of incremental evolution to display positive network characteristics with regards to both efficient resource use and robustness. This thesis focuses on applying the knowledge of what makes these ecosystems successful, as well as the techniques ecologists use to characterize them, to identify Net Zero (NZ) modifications that can simultaneously improve both of the aforementioned design objectives. First, a dataset of NZCs including quantitative energy and water flows throughout each case is constructed. This dataset then enables the use of Ecological Network Analysis on NZC networks, specifically identifying Finn’s Cycling Index (FCI) and Degree of System Order (DoSO) as metrics corresponding to sustainable and robust design, respectively. The results show that for the NZ modifications tested, a strong correlation exists between FCI and NZ performance, suggesting that FCI can be used as a proxy for sustainable network behavior. Additionally, a negative correlation emerges between FCI and DoSO. This result is significant as lower DoSO is indicative of improved network robustness, especially in the face of increasingly large disturbances, meaning that the modifications tested were in direct support of both sustainability and robustness. These findings hold true through disturbance testing, where the modified networks with higher NZ performance are also able to maintain the highest levels of operation during a disturbance. As such, this thesis provides proof of concept that bio-inspiration can be used to inform NZC design and impart improved sustainability and robustness into the networks.

Hairston, Garrett. (2022) “Using Bio-inspired Techniques to Design for Improved Sustainability and Robustness in Net Zero Communities.” MS, Mechanical Engineering, Texas A&M University.

Tyler’s thesis is titled: “Designing and Optimizing Supply Chain Networks for Resilience using Ecological Network Analysis”

Abstract: Traditional supply chain policies and design efforts, such as lean-manufacturing, prize efficiency over all other factors and are being challenged as supply chains struggle to bounce back from recent disruptions. Supply chain design guidelines that address resilience require a balanced approach between efficiency and redundancy, one that presents flexibility in a way that acknowledges profit requirements. Ecological Network Analysis reveals a unique balance of pathway efficiency and redundancy within ecosystems. This balance results in efficient steady-state operations and survival upon disruption. Supply chain networks are evaluated here using the same ecological analysis, providing design guidelines for achieving a balanced system-level resilience. Significant insights include that the validity of ecologically inspired supply chain design is contingent on the supply chain’s properties and that under the right conditions, the ecological balance of efficiency and redundancy can vastly improve the performance of supply chains during disruptions.

Wilson, Tyler. (2022) “Designing and Optimizing Supply Chain Networks for Resilience using Ecological Network Analysis.” MS, Mechanical Engineering, Texas A&M University.