Journal ArticleDOI
Modeling infrastructure resilience using Bayesian networks
TLDR
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.About:
This article is published in Computers & Industrial Engineering.The article was published on 2016-03-01. It has received 248 citations till now. The article focuses on the topics: Resilience (network) & Bayesian network.read more
Citations
More filters
Journal ArticleDOI
Review of quantitative methods for supply chain resilience analysis
TL;DR: This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity.
Journal ArticleDOI
Bayesian Networks in Fault Diagnosis
Baoping Cai,Huang Lei,Min Xie +2 more
TL;DR: Current gaps and challenges on use of BNs in fault diagnosis in the last decades with focus on engineering systems are explored and several directions for future research are explored.
Journal ArticleDOI
A Bayesian network model for resilience-based supplier selection
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.
Journal ArticleDOI
Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain†
TL;DR: The results reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction.
Journal ArticleDOI
Resilient supplier selection and optimal order allocation under disruption risks
Seyed Mohsen Hosseini,Nazanin Morshedlou,Dmitry Ivanov,M.D. Sarder,Kash Barker,Abdullah Al Khaled +5 more
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.
References
More filters
Journal ArticleDOI
A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities
Michel Bruneau,Stephanie E. Chang,Ronald T. Eguchi,George C. Lee,Thomas D. O'Rourke,Andrei M. Reinhorn,Masanobu Shinozuka,Kathleen J. Tierney,William A. Wallace,Detlof von Winterfeldt +9 more
TL;DR: In this article, the authors present a conceptual framework to define seismic resilience of communities and quantitative measures of resilience that can be useful for a coordinated research effort focusing on enhancing this resilience.
Book
An introduction to Bayesian networks
TL;DR: The principal ideas of probabilistic reasoning - known as Bayesian networks - are outlined and their practical implications illustrated and are intended for MSc students in knowledge-based systems, artificial intelligence and statistics, and for professionals in decision support systems applications and research.
Journal ArticleDOI
Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation
TL;DR: In this paper, three criteria for evaluating the performance of water resource systems are discussed, i.e., reliability, resilience, and vulnerability, which describe how likely a system is to fail, how quickly it recovers from failure, and how severe the consequences of failure may be.
Journal ArticleDOI
A review of definitions and measures of system resilience
Seyed Mohsen Hosseini,Kash Barker,Jose Emmanuel Ramirez-Marquez,Jose Emmanuel Ramirez-Marquez +3 more
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.