As a system complexity increases and technology innovation progresses rapidly, leasing the equipment is considered as an important issue in many engineering areas. In practice, many engineering fields lease the equipment because it is an economical way to lease the equipment rather than to own the equipment. In addition, as the maintenance actions for the equipment are costly and need a specialist, the lessor is responsible for the maintenance actions in most leased contract. Hence, the lessor should establish the optimal maintenance strategy to minimize the maintenance cost. This paper proposes two periodic preventive maintenance policies for the leased equipment. The preventive maintenance action of policy 1 is performed with a periodic interval, in which their intervals are the same until the end of lease period. The other policy is to determine the periodic preventive maintenance interval minimizing total maintenance cost during the lease period. In addition, this paper presents two decision-making models to determine the preventive maintenance strategy for leased equipment based on the lessor’s preference between the maintenance cost and the reliability at the end of lease period. The structural properties of the proposed decision-making model are investigated and algorithms to search the optimal maintenance policy that are satisfied by the lessor are provided. A numerical example is provided to illustrate the proposed model. The results show that a maintenance policy minimizing the maintenance cost is selected as a reasonable decision as the lease term becomes shorter. Moreover, the frequent preventive maintenance actions are performed when the minimal repair cost is higher than the preventive maintenance cost, resulting in higher maintenance cost.

As a system complexity increases and technology innovation progresses rapidly, it tends to lease a system rather than own one. This paper deals with a decision-making model to determine the preventive maintenance strategy for leased equipment. Various maintenance options are presented and formulated via the non-homogeneous Poisson process. During the lease period, the preventive maintenance strategy that minimizes the total cost among the presented maintenance options is selected. A numerical example is provided to illustrate the proposed model.

This paper describes the Bayesian and non-Bayesian approach for reliability demonstration test based on the samples from a finite population. The Bayesian approach involves the technical method about how to combine the prior distribution and the likelihood function to produce the posterior distribution. In this paper, the hypergeometric distribution is adopted as a likelihood function for a finite population. The conjugacy of the beta-binomial distribution and the hypergeometric distribution is shown and is used to make a decision about whether to accept or reject the finite population. The predictive distribution of the beta-binomial distribution is shown and will be used for the reliability demonstration test. A numerical example is also given.

The rapid growth of engineering technology and the emergence of systemized and large-scale engineering systems have resulted in complexity and uncertainty throughout the lifecycle activities of engineering systems. This complex and large-scale engineering system consists of numerous components, but system failure can be caused by failure of any one of a number of components. There is a real difficulty in managing such a complex and large-scale system as a part. In order to efficiently manage the system and have high reliability, it is necessary to structure a system with a complex structure as a sub-system. Also, in the case of a system in which cause of failures exist at the same time, it is required to identify the correlation of the components lifetime and utilize it for the design policy or maintenance activities of the system. Competitive risk theory has been used as a theory based on this concept. In this study, we apply the competitive risk theory to the models with combined structure of series and parallel which is the basic structure of most complex engineering systems. We construct a competing risks model and propose a mathematical model of net lifetime and crude lifetime for each cause of failure, assuming that the components consisting a parallel system are mutually dependent. In addition, based on the constructed model, the correlation of cause of failure is mathematically analyzed and the hazard function is derived by dividing into net lifetime and crude lifetime.

This paper describes the Bayesian approach for reliability demonstration test based on the sequential samples from the one-shot devices. The Bayesian approach involves the technical method about how to combine the prior distribution and the likelihood function to produce the posterior distribution. In this paper, the binomial distribution is adopted as a likelihood function for the one-shot devices. The relationship between the beta-binomial distribution and the Polya’s urn model is explained and is used to make a decision about whether to accept or reject the population of the one-shot devices by one by one then in terms of the faulty goods. A numerical example is also given.

The major issue in aircraft industry is aviation safety management because of demand improvement and advancement of the aircraft. This study is to assess the risks for aviation safety management using IRPN( importance-risk priority number) which is added to importance coefficient from RPN(risk priority number). In FMEA, RPN requires the factors like the occurrence (O), Severity (S), and Detection (D) of each failure mode to be precisely evaluated. Therefore, the severity and detection is derived by the value from FMEA of expert group based on the Fuzzy theory. The occurrence is calculated from the accident statistics of IATA(international air transport association) database. Particularly, this study introduces importance coefficient to prevent from RPN distortions. It is also derived from FMEA based on the Fuzzy theory. Finally, the critical ranking of risk factors according to I-RPN is compared with the existing research.

This study is to develop a mathematical analysis model to grasp the behaviors of cartels. Cartels are formed tacitly and cause tremendous damage to consumers in modern society which is composed of advanced industry structure. The government authorities have instituted the leniency programs to respond cartels. However, cartels will continue unless there are legal sanctions against cartels based on an accurate analysis of leniency programs. The proposed cartel equilibrium analysis model is a mathematical behavior model which is based on the existing methods and the prison’s dilemma of game theory. Therefore, the model has a form of pay off matrix of two players. We use a iterated polymatrix approximation (IPA) method to deduct a Nash equilibrium point. The model is validated by an empirical analysis as well.

In this paper, a mathematical model is developed for finding a degree of influence of risks affecting the project. At risk identification step, all risks are structured in forms of block diagrams after risks of project activities are identified. At risk analysis step, reliability functions are derived based on block diagrams of risks. Reliability function represents the probability of the project success. Based on the derived reliability functions, risks importance is defined as a criterion for expressing a degree of influence of each risk affecting the success of the project, which is. Risks importance includes the structural importance of risk, the probability of risk, and the impact of risk. It could be applied to determine the priorities of risks which should be controlled, and the use of such priorities could guarantee successful project activities. Finally, examples of the proposed mathematical models will be given.

This paper describes the Bayesian approach for reliability demonstration test based on the samples from a finite population. The Bayesian approach involves the technical method about how to combine the prior distribution and the likelihood function to pro

We want to accept or reject a finite population with reliability demonstration test. In this paper, we will describe Bayesian approaches for the reliability demonstration test based on the samples from a finite population. The Bayesian method is an approach that prior distribution and likelihood function combine to from posterior distribution. When we select somethings in a samples, we consider hypergeometric distribution. In this paper, we will explain the conjugacy of the beta-binomial distribution and hypergeometric distribution. The purpose of this paper is to make a decision between accept and reject in a finite population based on the conjugacy of the beta-binomial distribution.

  Most organizations have recently put more emphasis on the improvement in service quality. This phenomenon could come from the recognition that service quality plays an essential role in enhancing organization"s business performance. Hence this paper pre

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application.

This paper is intended to develop a Bayesian decision model for the repair of deteriorating system. A non-homogeneous Poisson process with a power law failure intensity function is used to describe the behavior of the deteriorating repairable system. The decision on whether to have minimal repair or imperfect repair should be made on the occurrence of a failure. However, it is difficult to make a reasonable decision due to many uncertainties intrinsic in repair actions. In this paper, prior distributions are used in order to analyze the uncertainties embedded in the decision alternatives. Especially, a prior distribution for imperfect repair with probabilistic reduction in the failure intensity is proposed. In addition, mathematical expressions to calculate the expected prior loss of each repair alternative are proposed.

In this paper we extend the classical decision model under uncertainty to a more general case. We propose an expected utility-uncertainty model and we can make a decision by trading off between a measure of uncertainty and a measure of expected value. As a risk analysis model, the expected utility-uncertainty model can be seen to be reasonable and flexible for states of nature or individuals' preferences. Moreover, the model can explain some decision paradoxes.

This paper is intended to compare the hazard rate estimations from Bayesian approach and maximum likelihood estimate(MLE) method. Hazard rate frequently involves unknown parameters and it is common that those parameters are estimated from observed data by

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application.

In this paper we extend the classical decision model under uncertainty to a more general case. We propose an expected utility-uncertainty model and we can make a decision by trading off between a measure of uncertainty and a measure of expected value. As a risk analysis model, the expected utility-uncertainty model can be seen to be reasonable and flexible for states of nature or individuals' preferences. Moreover, the model can explain some decision paradoxes.

This paper is intended to develop a Bayesian decision model for the repair of deteriorating system. A non-homogeneous Poisson process with a power law failure intensity function is used to describe the behavior of the deteriorating repairable system. The decision on whether to have minimal repair or imperfect repair should be made on the occurrence of a failure. However, it is difficult to make a reasonable decision due to many uncertainties intrinsic in repair actions. In this paper, prior distributions are used in order to analyze the uncertainties embedded in the decision alternatives. Especially, a prior distribution for imperfect repair with probabilistic reduction in the failure intensity is proposed. In addition, mathematical expressions to calculate the expected prior loss of each repair alternative are proposed.

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

This paper is intended to assess a dynamic system reliability. Bayesian networks, however, have difficulties in their application for assessing the system reliability especially when the system consists of dependent components and the probability of failu