Kash Barker

TL;DR: This paper presents a review of recent research articles related to defining and quantifying resilience in various disciplines, with a focus on engineering systems and provides a classification scheme to the approaches, focusing on qualitative and quantitative approaches and their subcategories.

TL;DR: Two resilience-based component importance measures are provided, and an algorithm to perform stochastic ordering of network components due to the uncertain nature of network disruptions, are illustrated with a 20 node, 30 link network example.

TL;DR: This paper offers a means to quantify resilience as a function of absorptive, adaptive, and restorative capacities with Bayesian networks, a popular tool to structure relationships among several variables.

TL;DR: A Bayesian network (BN) paradigm is proposed, a paradigm that effectively models the causal relationships among variables but that has not been used in the context of supplier evaluation and selection, to quantify the appropriateness of suppliers across primary, green, and resilience criteria.

TL;DR: A stochastic bi-objective mixed integer programming model is proposed to support the decision-making in how and when to use both proactive and reactive strategies in supplier selection and order allocation and can benefit suppliers to find the optimal set of operational decisions that enhance their resilience capabilities.