Search

This article presents a newly developed evolutionary algorithm for solving multi-objective optimization models for the design of multi-state two-terminal networks. It is assumed that for each network component, a known set of functionally equivalent component types (with different performance specifications) can be used to provide redundancy. Furthermore, the reliability behavior of the network and its components can have a range of states varying from perfect functioning to complete failure; that is, a multi-state behavior. Thus, the new algorithm allows solving the multi-objective optimization case of the reliability allocation problem for general multi-state two-terminal networks. The optimization routine is based on three major steps that use an evolutionary optimization approach and Monte Carlo simulation to generate a Pareto optimal string of probabilistic solutions to these problems. Examples for different multi-state two-terminal networks are used throughout the article to illustrate the approach. The results obtained for test cases are compared with other proposed methods to show the accuracy of the algorithm in generating approximate Pareto optimal sets for problems with a large solution space. Keywords: Multi-state reliability, allocation, multi-objective optimization, two-terminal networks, 1. Introduction The optimal design of networks is a classic optimization problem in the area of system reliability engineering (Jan et al., 1993; Dengiz et al., 1997; Konak and Smith, [...]

The mobile ad hoc wireless network (MAWN) promises to become an ever-present technology with application to a myriad of areas. This networking scheme has its roots in DoD technology, yet, the ability to measure and calculate its reliability is largely absent. This paper describes the unique attributes of the MAWN and how the classical methods for analysis of network reliability must be adjusted. The methods developed acknowledge the dynamic and scalable nature of the new networking scheme along with its absence of infrastructure and remove the need to a priori define a network configuration. The methods rely on a novel modeling approach that recognizes the effects of mobility on the formation of the network's configurations. Hence, this paper proposes a Monte Carlo simulation method that accounts for node mobility, node reliability and node performance in the network's connectivity and resultant network reliability. Keywords: Ad hoc networks, MANET, reliability, Monte Carlo, mobile computing, 1. Introduction Perkins and Bhagwat (2001) and Toh (2002) were the first to describe the Mobile Ad hoc Wireless Network (MAWN) which is a rapidly developing technology. These authors provide [...]

The installation of parallel-connected dc/dc converters allows energy and cost saving in photovoltaic (PV) systems. This paper develops a methodology with several algorithms to study the annual energy saving obtained with paralleled dc/dc converters in any world location. Moreover, this methodology allows one to select the minimum-cost size of a stand-alone PV system with dc/dc converter paralleling. For this purpose, a power-system sizing computer tool has been used, but its initial capabilities have been extended. The developed methodology allows for decision taking before investment is made. Index Terms--Converter paralleling, cost saving, decision capabilities, photovoltaic (PV) power systems, simulation tool.

The computation of the reliability of weighted voting systems is an important problem in reliability theory due to its potential application in security, target identification, safety and monitoring areas. Voting systems are used in a wide variety of applications where an acceptance or rejection decision has to be made about a binary proposition presented to the system. For these systems, it is of interest to obtain the probability so that based on the vote of decision-making units, the system aggregates these votes into the right decision when presented with such a proposition. This paper presents a holistic work on weighted voting system reliability by presenting modeling, computation, estimation and optimization techniques. The modeling part takes advantage of the structure of weighted voting systems to present a model of its reliability as a multi-state system. Next, based on the multi-state view of the system, an exact computational approach based on multi-state minimal cut and path vectors is introduced. The paper then acknowledges the computational complexity of the problem and provides a Monte Carlo simulation approach that estimates system reliability accurately and in an efficient computational time. Finally, an optimization heuristic that generates quasi-optimal solutions is presented that is able to solve the problem of maximizing the reliability of a weighted voting system based on a specified number of decision-making units with known reliability characteristics. Keywords: Multi-state reliability, voting systems, simulation, optimization, 1. Introduction Decision-making systems have recently become an area of intense activity because of their potential for application to areas such as security, target identification, and safety and monitoring (Kam [...]

A report on the use of simulation to generate network configurations that are evaluated with exact algorithms to investigate if they correspond to success or failure, using commercially available software to generate a classification tree, showed the technique to be effective, simple and widely applicable is presented.

The computation of the reliability of two-terminal networks is a classical reliability problem. For these types of problems, one is interested, from a general perspective, in obtaining the probability that two specific nodes can communicate. This paper presents a holistic algorithm for the analysis of general networks that follow a two-terminal rationale. The algorithm is based on a set replacement approach and an element inheritance strategy that effectively obtains the minimal cut sets associated with a given network. The vast majority of methods available for obtaining two-terminal reliability are generally based on assumptions about the performance of the network. Some methods assume network components can be in one of two states: (i) either completely failed; or (ii) perfectly functioning, others usually assume that nodes are perfectly reliable and thus, these methods have to be complemented or transformed to account for node failure, and the remaining methods assume minimal cut sets can be readily computed in order to analyze more complex network and component behavior. The algorithm presented in this paper significantly differs from previous approaches available in the literature in the sense that it is based on a predecessor matrix and an element substitution technique that allows for the exact computation of minimal cut sets and the immediate inclusion of node failure without any changes to the pseudo-code. Several case networks are used to validate and illustrate the algorithms. Keywords: Reliability, network, two-terminal, minimal cut sets, 1. Introduction The problem of obtaining the reliability of networks has been extensively researched during the past decades. Within this area of research, one of the classical network reliability problems [...]

The two-terminal reliability problem assumes that a network and its elements are either in a working or a failed state. However, many practical networks are built of elements that may operate in more than two states i.e., elements may be degraded but still functional. Multistate two-terminal reliability at demand level d (M2T[R.sub.d]) can be defined as the probability that the system capacity generated by multistate components is greater than or equal to a demand of d units. This paper presents a fully multistate-based algorithm that obtains the multistate equivalent of binary path sets, namely, Multistate Minimal Path Vectors (MMPVs), for the M2T[R.sub.d] problem. The algorithm mimics natural organisms in the sense that a select number of arcs inherit information from other specific arcs contained in a special set called the 'primary set.' The algorithm is tested and compared with published results in the literature. Two features of the algorithm make it relevant: (i) unlike other approaches, it does not depend on an a priori knowledge of the binary path sets to obtain the MMPVs; and (ii) the use of an information sharing approach and network reduction technique significantly reduce the number of vector analyses needed to obtain all the component levels that guarantee system success. Additionally, the complexities associated with the computation of reliability are discussed. A Monte Carlo simulation approach is used to obtain an accurate estimate of actual M2TR values based on MMPVs. Examples are used to validate the algorithm and the simulation procedure., 1. Introduction System-reliability evaluation can be understood as the computation of the probability that a system is functioning for a given amount of time under specified conditions (Elsayed, 1996). In [...]

New bounding strategies are presented to improve the confidence interval estimation for system reliability based on component level reliability and associated uncertainty data. The results show a significant improvement in the accuracy of the confidence intervals for the system reliability when compared with existing approximation methods, while the bounding procedure is simple, effective, and widely applicable.

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., 1. Introduction The redundancy allocation problem has previously been studied considering numerous different problem formulations and solution methodologies. In a new approach, the problem of redundancy allocation has been formulated [...]