International Journal of Reliability, Quality and Safety Engineering

Here we have considered an n-standby system with one and more than one repair facility (ies) for interference (or stress–strength) models. The system is working under impacts of stresses. Each impact is called a cycle. When a component fails it goes for repair; the repair policy is first-come-first-serve (FCFS). The failure times of components, system and repair times all are measured in cycles. The reliability of the system at the Nth cycle is evaluated in different cases. When stress–strength distributions both are either exponential or gamma or normal the reliability of the system is numerically evaluated for given n, N and for some particular values of the parameters involved and are tabulated. The numerical values of the system reliability are on the expected line.

This paper deals with the application of nonhomogeneous semi-Markov processes in reliability modeling. The notion of a semi-Markov embeddable reliability structure is introduced and discussed. As an example, load-sharing k-out-of-n : G systems of equal components with arbitrary life distributions under the equal load-sharing rule are treated in the context of semi-Markov embeddable systems. Besides some discussion about load-life models, the case of repairable components is to some extent treated as well.

This paper presents a new approach to optimizing the cutting glass fiber with multiple performance characteristics, based on reliability analysis, Taguchi, and fuzzy logic. The speed, volume, and cutting load parameters were optimized during the cutting process with the application of the Weibull modulus to the blade wear. In this study, optimization with multiple performance characteristics was found to be the highest cutting speed and smallest cutting volume, and highest cutting load. An analysis of variance (ANOVA) of the multiple performance characteristic indicates that the cutting speed (37.22%), the cutting volume (26.74%), and the cutting load (20.50%) are the crucial parameters in the cutting process of glass fibers. In summary, the optimal cutting parameter should be A3B1C2. The experimental results are presented to demonstrate the effectiveness of this approach.

A software hazard rate model is known as one of the important and useful mathematical models for describing the software failure occurrence phenomenon observed in a testing phase. It is difficult to say that the testing environment always constant during a testing phase due to changing the specification and fault target and so forth. Therefore, taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment. In this paper, we develop extended software hazard rate models based on well-known Jelinski–Moranda and Moranda models, by considering with a change of testing environment. Especially in this paper, we incorporate the uncertainty of the effect of the change on the software reliability growth process into the software hazard rate modeling. Finally, we show numerical examples for our models and results of model comparisons by using actual data.

Warranty as a kind of service contract plays a key role in business and legal transactions today. In this paper, we present for the first time a tri-partite service contract model including a manufacturer, an agent and a customer under risk parameter. We determine an optimal sale price, a warranty period and a warranty price for the manufacturer under quantity discount policies. The optimal maintenance cost or repair cost is obtained by the maximizing of an agent's profit according to the penalty cost incurred due to waiting time. Moreover, the customer maximizes his/her satisfaction by purchasing several products and choosing a portfolio of service contracts. Whereas the risk-aversion parameter on the customer side has an impact on their decision for choosing the type of service contract. On the other hand, the discount rate regime on the manufacturer side influences the number of purchased products. We present some numerical examples to illustrate the working logic of our model.