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The purpose of this paper is to derive the user‐perceived availability of M‐for‐N shared protection systems composed of multiple user groups, each with a protection‐switching priority.

The paper assumes a shared protection system with M protection units and N working units. The memoryless state transition diagram viewed from the system administrator, combined with combinatorial analysis of state probabilities on protection switching, yields a generic formula of the availability viewed from an arbitrary end user.

The numerical examples of availability reveal the effect of prioritized protection switching. It is observed that the total protection capacity is constant regardless of the ways of priority grouping. The shared protection system with multiple protection units enables more flexible availability allocation compared with the case of a single protection unit.

User‐perceived reliability is still an unexplored research area. Many variations of the system treated in this paper can be applied to various applications.

The analysis provides useful information for the design and operation of, for example, telecommunication network devices. The analysis is applicable to general shared protection systems that are subject to service level agreement (SLA) involving user‐perceived reliability measures.

This paper establishes the model of the priority shared protection systems for the first time and shows a practical computation method of prioritized user‐perceived availability.

Asset intensive process industries are under immense pressure to achieve promised return on investments and production targets. This can be accomplished by ensuring the highest level of availability, reliability and utilization of the critical equipment in processing facilities. In order to achieve designed availability, asset characterization and maintainability play a vital role. The most appropriate and effective way to characterize the assets in a processing facility is based on risk and consequence of failure. The paper aims to discuss these issues.

In this research, a risk-based stochastic modeling approach using a Markov decision process is investigated to assess a processing unit's availability, which is referred as the risk-based availability Markov model (RBAMM). RBAMM will not only provide a realistic and effective way to identify critical assets in a plant but also a method to estimate availability for efficient planning purposes and resource optimization.

A unique risk matrix and methodology is proposed to determine the critical equipment with direct impact on the availability, reliability and safety of the process. A functional block diagram is then developed using critical equipment to perform efficient modeling. A Markov process is utilized to establish state diagrams and create steady-state equations to calculate the availability of the process. RBAMM is applied to natural gas absorption process to validate the proposed methodology. In the conclusion, other benefits and limitations of the proposed methodology are discussed.

A new risk-based methodology integrated with Markov model application of the methodology is demonstrated using a real-life application.

The purpose of this paper is to propose a novel hybrid approach called as Markov System Dynamics (MSD) approach which combines the Markov approach with system dynamics (SD) simulation approach for availability modeling and to study the dynamic behavior of repairable systems.

In the proposed approach the identification of the single unit repairable system all possible states has been performed by using the Markov approach. The remaining stages of traditional Markov analysis are highly mathematically intensive. The present work proposes a hybrid approach called as MSD approach which combines the Markov approach with SD simulation approach to overcome some of the limitations of Markov process in a simple and efficient way for availability modeling and to study the dynamic behavior of this system.

The proposed framework is illustrated for a single unit repairable system. The worked out example shows the steady state point and also it gives the point, interval and steady state availabilities and also the dynamic behavior of the system. However this methodology can be extended easily for more complex multi-state maintainable systems. The results of the simulation when compared with that obtained by traditional Markov analysis clearly validate the proposed approach as an alternative approach for availability modeling of repairable systems.

In many practical situations we require to find the time at which our system reaches steady state conditions for planning maintenance activities. The proposed MSD method in this paper is capable of finding this steady state point very easily.

The proposed approach clearly indicates the time at which the system reaches its steady state and calculates the point, interval availabilities for planning maintenance activities. The different parties, i.e., engineers and machine operators, can jointly work with this model in order to understand the dynamic behavior of repairable systems.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

This article examines the underlying paradigm of the total management process of productivity so as to observe the underlying drives. Given the variety of conditions prevailing in developing countries, an organisation may take a number of concepts and alternatives to improve its ability to maintain values and to create values for the future. It has been hypothesised that productivity may be enhanced by recognising the need to maximise the availability of organisational assets.

Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues.

This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring.

This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective.

The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs.

A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

Hospitals procure high volumes of medical supplies through large distributors in order to leverage economies of scale. However, when shortages hit, hospitals incur high penalty costs by purchasing from secondary markets. In this paper, the authors counter the hospital's typical purchasing strategy that a collaborative relationship with a large, Tier I medical supply distributor is beneficial under all conditions. The paper finds that during shortages the more beneficial strategy is for the hospital to add a medium-sized, Tier II distributor who offers a transactional relationship and is willing to provide a “preferred allocation” in return for a pre-committed annual purchase contract. The paper aims to discuss these issues.

The authors assume availability of order volume to be a stochastic process and formulate the problem as a two-stage stochastic programming model, with optimal allocation in the second stage. The authors analyze the first-stage objective function using full-factorial numerical experimentation and perform a complete search for optimal volume mix. In addition, the model accounts for purchasing relationship, shortage cost, and varying price discount schedules.

Under no shortage situation, hospitals purchase its entire order volume from Tier I distributor. However, during shortages, for any increase in preferred allocation from the Tier II distributor, hospitals purchase high volumes from the Tier II distributor except when preferred allocation and availability is high. The paper finds that the average cost savings for the use of preferred allocation is 16.14 percent.

Existing purchasing literature focusses on the benefit of using single/multiple homogenous distributors under all conditions. In this paper, the authors examine the benefit of using non-homogenous distributors under conditions of shortage when one of them is willing to provide preferred allocation under varying price discount schedules.

This paper aims to analyse the impact of university libraries by investigating the availability of the references of dissertations in the university's own library, special attention being given to the electronic availability of cited works.

Citation analysis was used. Ten dissertations of economics and administration from two Finnish universities were analysed. The dissertations were completed in 2005‐2006. The comparison of the availability of references in two different universities – i.e. a large, multidisciplinary university and a small, specialised university – was carried out.

The availability of the references of dissertations was good in both universities. A large number of references, especially journal articles, were already available electronically. The university libraries have a significant role in contributing to doctoral studies because they offer access to adequate information resources.

The sample is quite limited: only two Finnish universities and ten dissertations were included in the data. The good availability of references might result from doctoral students' unwillingness to search outside sources. However, doctoral students do not cite all sources that they read. The impact of the library on doctoral studies could be even bigger than can be shown by citation analysis.

The paper indicates that citation analysis is a good tool in promoting the library's impact.

The paper emphasises that the impact of libraries can be demonstrated by presenting the visibility of their collections in dissertations. Because of the rapid change of information environment, the study considered the availability of electronic sources of dissertations. An interesting comparison between two different universities was carried out.

The purpose of this article is to explore the concept of availability, both empirically and theoretically, in the context of three Swedish organisations, and identifies the structural influences on availability patterns for work and family.

The article is based on quantitative case studies using employer records and an employee questionnaire in three organisations. Multivariate descriptive statistics and multivariate logistic regression are used to illustrate and analyse patterns of availability for work and family.

The descriptive data demonstrate the influence of the organisational context and type of production process, as well as gender, on availability patterns. Patterns of work availability appeared to differ across the organisations to a greater extent than patterns of family availability, which were highly gendered. The logistic regression results indicated that: occupation was a significant influence on both temporal and spatial availability patterns across the organisations; gender was the most significant influence on time spent on household work and part‐time working for parents with young children; age of employees and age of employees' children were the most significant factors influencing the use of time off work for family.

Analysis limited to case studies. More extensive quantitative research would be needed to make empirical generalisations. Qualitative research would be needed to establish whether and how employees are able to make use of different availability patterns to improve their work‐life balance.

The concept of availability is a new way of trying to capture and analyse tensions in people's everyday lives as they try to manage multiple demands.

The purpose of this paper is to propose a state dependent stochastic Markov model for availability analysis of process plant instead of traditional time dependent model.

The traditional concepts of system performance measurement and reliability (namely, binary; two-state concepts) are observed to be inadequate to characterize performance of complex system components. Availability analysis considering an intermediate state, such as a degraded state, provides a better alternative mechanism for system performance mapping. The availability model provides a better assessment of failure and repair characteristics for equipment in the sub-system and its overall performance. In addition to availability analysis, this paper also discusses the preventive maintenance (PM) program to achieve target availability. In this model, the degraded state is considered as a PM state. Using Markov analysis the optimum maintenance interval is determined.

Markov process provides an easier way to measure the performance of the process facility. This study also revealed that the maintenance interval has a major influence in the availability of a process facility as well as in maintaining target availability. The developed model is also applicable to the varying target availability as well as having the capability to handle even the reconfigured process systems.

Considering the degraded state as an operative state, a higher availability of the plant is predicted. The consideration of the degraded state of the system makes the availability estimation more realistic and acceptable. Availability quantification, target availability allocation and a PM model are exemplified in a sub-system of an liquefied natural gas facility.

The unique features of the present study are; Markov modeling approach integrating availability and PM; optimum PM interval determination of stochastically degrading components based on target availability; consideration of three-state systems; and consideration of increasing failure rates.