Managing Pandemic Resilience with Other Cascading Disruptions of a Socio-Technical System

The COVID-19 pandemic spurred the development of methodologies to assess risk to economic development plans. To increase local recovery efforts, the federal government provides funding for regional economic development. Funds are allocated based on immediate needs as well as growth potential. This work advances the risk register methodology to prioritize infrastructure initiatives - potential projects, policies, or other actions an organization may take - while considering the influence of exogenous scenarios on priorities given the impact of COVID-19. The risk register identifies performance criteria which measure performance - for example, an initiative incentivizing restaurants to increase outdoor seating improves a create new jobs criterion. Next, the register identifies disruptive events and groups these events into scenarios. There are now two sets of data: the initiatives considered for implementations, and a set of disruptive scenarios, including a baseline. The register evaluates the impact of each scenario on each initiative. For each scenario, the initiative with greatest impact on performance criteria is ranked first, and so on for the remaining scenarios. These rankings mathematically capture the influence of each scenario on the priority of each initiative. The risk register mathematically quantifies the disruptiveness of each scenario, allowing the comparison of different disruptive events. This information can help determine how to allocate resources to improve system resilience. The risk register methodology is applied to a socio-technical system of systems. This work advances methods outlined in the Systems Engineering Body of Knowledge, specifically the System of Systems knowledge area. © 2022 IEEE.

Why System Requirements Must Influence the Selection of Causal Models to Address Congestion in Freight Logistics

There is urgent need to identify goods which may be diverted to an alternative mode of freight movement due to the disruptions to priorities and decisions of logistics entities by Covid-19, container shortages, labor shortages, etc. Here diversion refers to the shift of goods in the logistics network that are typically moved via one freight mode - such as rail, highway, or air - to another mode. There has been significant effort to model this behavior for purposes of infrastructure planning by both national and regional transportation agencies. In the United States this has culminated in a gold standard behavioral/agent-based model supported by the Federal Highway Administration (FHWA) and implemented by state transportation agencies. This model is well established and demonstrated to successfully predict the diversion of goods from one mode to another in response to changes in the freight network. However, this model is limited to consideration of the requirements state or national transportation agencies value. As a result, the model requires large teams, years of work, and millions of dollars in funding. This scale is not acceptable for modeling of transient events such as container shortages which would dissipate before model completion. This work advances a novel approach to model this behavior that is sensitive to the requirements of a diverse set of stakeholders and conditions. © 2022 IEEE.