Abstract
While traditional risk analysis quantifies the likelihoods and consequences of adverse events, this is not always possible under conditions where probabilities of occurrence and magnitude of consequences cannot be reliably quantified due to high degrees of uncertainty. Instead, this work takes a perspective of risk sources as scenarios, consisting of emergent and future conditions. Scenarios are modeled as causing two key distinct modes of disruption in the context of project schedules: (i) preferences – updates in the relative emphasis placed on project management success criteria, (ii) structure – updates in the configuration and composition of the project network and parameters of constituent activities. These disruptions impact the measured performance of project state variables in terms of activity-level and overall project duration, cost, and quality. No work to date has investigated the joint and several effects of disruptions to changes in the preferences and in the structure. Effort in risk analysis is needed to identify and prioritize both the most disruptive scenarios and the system elements that are most affected by disruptions. Integrating perspectives of risk analysis, systems engineering, and project management, the dissertation develops and tests methods that combine analysis of modes of disruptions with a focus on scheduling. The several modes are shown to reorder the constituent activities of project schedules subject to typical precedence constraints. Scenarios are herein assessed by their influence to prioritizations of project activities. The key research questions include how particular scenarios, consisting of emergent and future conditions, are more disruptive than others due to their re-prioritization of project activities. Results are intended to provide guidance for subsequent risk management decision making, guiding operations and planning to avoid deviations from programmatic targets, improving allocation of resources to risk mitigations in complex projects, and informing what additional analyses must be performed. The approach is demonstrated in several examples: construction of large-scale infrastructure, hurricane response, and e-commerce product design and assembly, each vulnerable to scenarios comprised of emergent and future conditions including technology, economy, demographics, markets, regulation, environment, behaviors, etc. The approach is transferable across applications of systems engineering and risk analysis, including environment, health, commerce, technology development, and others. Future work is recommended to combine analyses with other modes of disruption, and to develop decision modeling methodologies which leverage these results to support risk management decision making.
