Jose Emmanuel Ramirez-Marquez

Generic metrics and quantitative approaches for system resilience as a function of time

Resilience is generally understood as the ability of an entity to recover from an external disruptive event. In the system domain, a formal definition and quantification of the concept of resilience has been elusive. This paper proposes generic metrics and formulae for quantifying system resilience. The discussions and graphical examples illustrate that the quantitative model is aligned with the fundamental concept of resilience. Based on the approach presented it is possible to analyze resilience as a time dependent function in the context of systems. The paper describes the metrics of network and system resilience, time for resilience and total cost of resilience. Also the paper describes the key parameters necessary to analyze system resilience such as the following: disruptive events, component restoration and overall resilience strategy. A road network example is used to demonstrate the applicability of the proposed resilience metrics and how these analyses form the basis for developing effective resilience design strategies. The metrics described are generic enough to be implemented in a variety of applications as long as appropriate figures-of-merit and the necessary system parameters, system decomposition and component parameters are defined.

A review of definitions and measures of system resilience

Modeling and evaluating the resilience of systems, potentially complex and large-scale in nature, has recently raised significant interest among both practitioners and researchers. This recent interest has resulted in several definitions of the concept of resilience and several approaches to measuring this concept, across several application domains. As such, this paper presents a review of recent research articles related to defining and quantifying resilience in various disciplines, with a focus on engineering systems. We provide a classification scheme to the approaches in the literature, focusing on qualitative and quantitative approaches and their subcategories. Addressed in this review are: an extensive coverage of the literature, an exploration of current gaps and challenges, and several directions for future research.

Composite importance measures for multi-state systems with multi-state components

This paper presents & evaluates composite importance measures (CIM) for multi-state systems with multi-state components (MSMC). Importance measures are important tools to evaluate & rank the impact of individual components within a system. For multi-state systems, previously developed measures do not meet all user needs. The major focus of the study is to distinguish between two types of importance measures which can be used for evaluating the criticality of components in MSMC with respect to multi-state system reliability. This paper presents Type 1 importance measures that are involved in measuring how a specific component affects multi-state system reliability. A Monte Carlo (MC) simulation methodology for estimating the reliability of a MSMC is used for computing the proposed CIM metrics. Previous approaches (Type 2) have focused on investigating how a particular component state or set of states affects multi-state system reliability. For some systems, it is not clear how to prioritize system component importance, collectively considering all of its states, using the previously developed importance measures. That detracts from those measures. Experimental results show that the proposed CIM can be used as an effective tool to assess component criticality for MSMC. Examples are used to illustrate & compare the proposed CIM with previous multi-state importance measures.

Resilience-based network component importance measures

Disruptive events, whether malevolent attacks, natural disasters, manmade accidents, or common failures, can have significant widespread impacts when they lead to the failure of network components and ultimately the larger network itself. An important consideration in the behavior of a network following disruptive events is its resilience, or the ability of the network to “bounce back†to a desired performance state. Building on the extensive reliability engineering literature on measuring component importance, or the extent to which individual network components contribute to network reliability, this paper provides two resilience-based component importance measures. The two measures quantify the (i) potential adverse impact on system resilience from a disruption affecting link i, and (ii) potential positive impact on system resilience when link i cannot be disrupted, respectively. The resilience-based component importance measures, 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.

A generic method for estimating system reliability using Bayesian networks

This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.

Redundancy allocation for series-parallel systems using a max-min approach

The redundancy allocation problem is formulated with the objective of maximizing the minimum subsystem reliability for a series-parallel system. This is a new problem formulation that offers several distinct benefits compared to traditional problem formulations. Since time-to-failure of the system is dictated by the minimum subsystem time-to-failure, a logical design strategy is to increase the minimum subsystem reliability as high as possible, given constraints on the system. For some system design problems, a preferred design objective may be to maximize the minimum subsystem reliability. Additionally, the max-min formulation can serve as a useful and efficient surrogate for optimization problems to maximize system reliability. This is accomplished by sequentially solving a series of max-min subproblems by fixing the minimum subsystem reliability to create a new problem. For this new formulation, it becomes possible to linearize the problem and use integer programming methods to determine system design configurations that allow mixing of functionally equivalent component types within a subsystem. This is the first time the mixing of component types has been addressed using integer programming. The methodology is demonstrated on three problems.

Six sigma project selection using data envelopment analysis

Purpose – The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.Design/methodology/approach – This research provides the identification ...

Two-terminal reliability analyses for a mobile ad hoc wireless network

Reliability is one of the most important performance measures for emerging technologies. For these systems, shortcomings are often overlooked in early releases as the cutting edge technology overshadows a fragile design. Currently, the proliferation of the mobile ad hoc wireless networks (MAWN) is moving from cutting edge to commodity and thus, reliable performance will be expected. Generally, ad hoc networking is applied for the flexibility and mobility it provides. As a result, military and first responders employ this network scheme and the reliability of the network becomes paramount. To ensure reliability is achieved, one must first be able to analyze and calculate the reliability of the MAWN. This work describes the unique attributes of the MAWN and how the classical analysis of network reliability, where the network configuration is known a priori, can be adjusted to model and analyze this type of network. The methods developed acknowledge the dynamic and scalable nature of the MAWN along with its absence of infrastructure. Thus, the methods rely on a modeling approach that considers the probabilistic formation of different network configurations in a MAWN. Hence, this paper proposes reliability analysis methods that consider the effect of node mobility and the continuous changes in the networks connectivity.

Multi-state component criticality analysis for reliability improvement in multi-state systems

This paper evaluates and implements composite importance measures (CIM) for multi-state systems with multi-state components (MSMC). Importance measures are frequently used as a means to evaluate and rank the impact and criticality of individual components within a system yet they are less often used as a guide to prioritize system reliability improvements. For multi-state systems, previously developed measures sometimes are not appropriate and they do not meet all user needs. This study has two inter-related goals: first, to distinguish between two types of importance measures that can be used for evaluating the criticality of components in MSMC with respect to multi-state system reliability, and second, based on the CIM, to develop a component allocation heuristic to maximize system reliability improvements. The heuristic uses Monte-Carlo simulation together with the max-flow min-cut algorithm as a means to compute component CIM. These measures are then transformed into a cost-based composite metric that guides the allocation of redundant elements into the existing system. Experimental results for different system complexities show that these new CIM can effectively estimate the criticality of components with respect to multi-state system reliability. Similarly, these results show that the CIM-based heuristic can be used as a fast and effective technique to guide system reliability improvements.

Optimization of system reliability in the presence of common cause failures

Abstract The redundancy allocation problem is formulated with the objective of maximizing system reliability in the presence of common cause failures. These types of failures can be described as events that lead to simultaneous failure of multiple components due to a common cause. When common cause failures are considered, component failure times are not independent. This new problem formulation offers several distinct benefits compared to traditional formulations of the redundancy allocation problem. For some systems, recognition of common cause failure events is critical so that the overall system reliability estimation and associated design resembles the true system reliability behavior realistically. Since common cause failure events may vary from one system to another, three different interpretations of the reliability estimation problem are presented. This is the first time that mixing of components together with the inclusion of common cause failure events has been addressed in the redundancy allocation problem. Three non-linear optimization models are presented. Solutions to three different problem types are obtained. They support the position that consideration of common cause failures will lead to different and preferred “optimal” design strategies.