Building Reliable Budget-Based Binary-State Networks

Everyday life is driven by various network, such as supply chains for distributing raw materials, semi-finished product goods, and final products; Internet of Things (IoT) for connecting and exchanging data; utility networks for transmitting fuel, power, water, electricity, and 4G/5G; and social networks for sharing information and connections. The binary-state network is a basic network, where the state of each component is either success or failure, i.e., the binary-state. Network reliability plays an important role in evaluating the performance of network planning, design, and management. Because more networks are being set up in the real world currently, there is a need for their reliability. It is necessary to build a reliable network within a limited budget. However, existing studies are focused on the budget limit for each minimal path (MP) in networks without considering the total budget of the entire network. We propose a novel concept to consider how to build a more reliable binary-state network under the budget limit. In addition, we propose an algorithm based on the binary-addition-tree algorithm (BAT) and stepwise vectors to solve the problem efficiently

Development of a Parallel BAT and Its Applications in Binary-state Network Reliability Problems

Various networks are broadly and deeply applied in real-life applications. Reliability is the most important index for measuring the performance of all network types. Among the various algorithms, only implicit enumeration algorithms, such as depth-first-search, breadth-search-first, universal generating function methodology, binary-decision diagram, and binary-addition-tree algorithm (BAT), can be used to calculate the exact network reliability. However, implicit enumeration algorithms can only be used to solve small-scale network reliability problems. The BAT was recently proposed as a simple, fast, easy-to-code, and flexible make-to-fit exact-solution algorithm. Based on the experimental results, the BAT and its variants outperformed other implicit enumeration algorithms. Hence, to overcome the above-mentioned obstacle as a result of the size problem, a new parallel BAT (PBAT) was proposed to improve the BAT based on compute multithread architecture to calculate the binary-state network reliability problem, which is fundamental for all types of network reliability problems. From the analysis of the time complexity and experiments conducted on 20 benchmarks of binary-state network reliability problems, PBAT was able to efficiently solve medium-scale network reliability problems

Reliability of a Maintainable Manufacturing Network subject to Budget

Applying network analysis, a manufacturing system can be constructed as a manufacturing network by representing each workstation as an arc and each inspection station as a node. In particular, the capacity of each workstation is stochastic (i.e. multistate) due to the possibility of failure, partial failure, and maintenance. In practical cases, such a manufacturing network has to achieve a specified production level to satisfy the customers’ orders. Hence, maintenance is necessary to guarantee a manufacturing network can retain a minimal production level. A maintenance model, namely maintainable manufacturing network (MMN), is proposed to evaluate whether the manufacturing system can provide sufficient capacity subject to maintenance budget or not. The maintenance reliability is further proposed to calculate the probability that the MMN provides a sufficient capacity level to meet the minimal production level under maintenance budget

Efficient Reliability Modelling & Analysis of Complex Systems with Application to Nuclear Power Plant Safety

Nuclear power may be our best chance at a permanent solution to the world's energy challenges, owing to its sustainability and environmental friendliness. However, it also poses a great risk to life, property, and the economy, given the possibility of severe accidents during its generation. These accidents are a result of the susceptibility of the generating plants to component failure, human error, extreme environmental events, targeted attacks, and natural disasters. Given the complexity and high interconnectivity of the systems in question, a small glitch, otherwise known as an initiating event, could cascade to catastrophic consequences. It is, therefore, vital that the vulnerability of a plant to these glitches and their ensuing consequences be ascertained, to ensure that the appropriate mitigating actions are taken. The reliability of a system is the likelihood that it survives a defined period and its availability is the likelihood of it being capable of performing its required functions on demand. These quantities are important to a nuclear power plant's safety because, a nuclear power plant by default is equipped with safety systems to inhibit the propagation of an initiating event. An accident ensues if the safety systems required to mitigate some initiating event are unavailable or incapacitated by the initiating event. It is, therefore, easy to see that the reliability, as well as the availability of these systems, shape the safety of the plant. These crucial quantities, currently, are estimated using legacy techniques like static fault and event tree analyses or their derivatives. Despite their popularity and widely acclaimed success, these legacy techniques lack the flexibility to implement fully the operational dynamics of the majority of systems. Most importantly, their ease of application deteriorates with increasing system size and complexity, such that the analyst is often forced to make unrealistic assumptions. These unrealistic assumptions sometimes compromise the accuracy of the results obtained and subsequently, the quality of the risk management decisions reached. Their inadequacy is often amplified if the system is composed of multi-state components or characterised by epistemic uncertainties, induced by vague or imprecise data. The ideal approach, therefore, should be sufficiently robust to not necessitate unrealistic assumptions but flexible enough to accommodate realistic system attributes, while guaranteeing accuracy. This dissertation provides a detailed account of a series of computationally efficient system reliability analysis techniques proposed to address the limitations of the existing probabilistic risk assessment approaches. The proposed techniques are based mainly, on an advanced hybrid event-driven Monte Carlo simulation technique that invokes load-flow principles to resolve, intuitively, the difficulties associated with the topological complexity of systems and the multi-state attributes of their components. In addition to their intuitiveness and relative completeness, a key advantage of the proposed techniques is their general applicability. They have been applied, for instance, to a variety of problems, ranging from the production availability of an offshore oil installation and the maintenance strategy optimization of the IEEE-24 bus test system to the probabilistic risk assessment of station blackout accidents at the Maanshan nuclear power plant in Taiwan. The proposed techniques, therefore, should influence robust decisions in the risk management of not only nuclear power plants but other critical systems as well. They have been incorporated into the open-source uncertainty quantification tool, OpenCossan, to render them readily available to industry and other researchers

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Uncertainty in Engineering

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners