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Recognizing the inevitability of large-scale disruptions, emphasis in supply chain decision making has shifted from prevention and protection to resilience, or the ability to withstand, adapt to, and recover in a timely manner from a disruptive event. Recent work in supply chain resilience has primarily consisted of qualitative frameworks and lessons learned after disruptions. This project addresses resilient supplier selection, a significant concern across industry and government enterprises. This work develops a supplier selection decision framework that includes (i) a multi-objective optimization formulation for multi-sourced supplier selection and (ii) a means to address uncertainty underlying the occurrence of a disruptive event through a Bayesian network-driven measure of disruption likelihood. The model accounts for several resilience strategies such as increasing supplier capacity beyond normal levels as a mitigation strategy to fortify suppliers against disruption, connecting firms to a back-up supplier as a contingency strategy that allows the supplier to adapt its loss by reconfiguring the channels for the movement of materials, and a contingency strategy of having additional recovery resources to enable suppliers to restore lost capacity more quickly. ; Naval Postgraduate School Acquisition Research Program

Proceedings Paper (for Acquisition Research Program) ; Under support from the Acquisition Research Program and government/industry partnerships, previous research has successfully developed, tested, and implemented a system maturity measure (i.e., a system readiness level [SRL]); supporting optimization models; and an enhanced SRL hierarchy for multi-function, multi-capability (MFMC) systems. The later developments are predicated on what has become the accustomed challenge for managers and engineers to properly assess systems'' development and acquisition to ensure the achievement of critical capabilities and functions while deciding among multiple technologies with similar functionalities but different maturity levels and limited resources. Building on these developments, the proposed research responds to the question: How can we efficiently and effectively allocate available resources to ensure the maturity achievement of critical functions and capabilities in a MFMC system when facing competing technology alternatives? As an answer, we propose the development of multi-objective optimization models and solution approaches that can be used to evaluate systems'' development maturity, to track progress, to identify component criticality, and to form corresponding strategies for understanding trade-offs in technology and integration options. Further, this effort summarizes and document the previous and proposed research in cooperation with industry and government partners to produce a comprehensive guidebook for public distribution on the proper application of this research in methods, processes, and tools (MPT) for defense acquisition. ; Naval Postgraduate School Acquisition Research Program ; Approved for public release; distribution is unlimited.

AbstractNatural disasters affect thousands of communities every year, leaving behind human losses, billions of dollars in rebuilding efforts, and psychological affectation in survivors. How fast a community recovers from a disaster or even how well a community can mitigate risk from disasters depends on how resilient that community is. One main factor that influences communities' resilience is how a community comes together in times of need. Social cohesion is considered to be"the glue that holds society together, which can be better examined in a critical situation. There is no consensus on measuring social cohesion, but recent literature indicates that social media communications and communities play an essential role in today's disaster mitigation strategies.This research explores how to quantify social cohesion through social media outlets during disasters. The approach involves combining and implementing text processing techniques and graph network analysis to understand the relationships between nine different types of participants during hurricanes Harvey, Irma, and Maria. Visualizations are employed to illustrate these connections, their evolution before, during, and after disasters, and the degree of social cohesion throughout their timeline. The proposed measurement of social cohesion through social media networks presented in this work can provide future risk management and disaster mitigation policies. This social cohesion measure identifies the types of actors in a social network and how this network varies daily. Therefore, decisionmakers could use this measure to release strategic communication before, during, and after a disaster strikes, thus providing relevant information to people in need.

AbstractCritical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making.We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, "costs" of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.

Proceedings Paper (for Acquisition Research Program) ; With support from the Naval Postgraduate School and government/industry partnerships, the Systems Development & Maturity Laboratory (SD&ML) at Stevens Institute of Technology has successfully developed a systems maturity measure (i.e., System Readiness Level [SRL]) and supporting optimization models for inclusion in a Systems Earned Readiness Management methodology. We now believe it is time to spiral back to the beginning of the original developments of the SRL to enhance fundamental capabilities of assessing system maturity in order to address some recurring issues to its application. That is, systems have variants in their physical architecture that realize certain functionality and capability by which trade-off decisions are made to find a satisficing solution for a deployable system. This paper enhances previously developed methodologies by addressing this fundamental question, ''What are the trades-off in functionality, capability, cost, schedule, and maturity that will allow the deployment of a less-than-fully mature system that can still satisfy specific needs of the warfighter?'' To answer this question, we formulate a capability-specific SRL and use multi-dimensional component importance analysis to identify which components of the system should receive the most application of resources when they are constrained. ; Naval Postgraduate School Acquisition Research Program ; Approved for public release; distribution is unlimited.