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The purpose of this paper is to investigate age replacement policies for two-component parallel system with stochastic dependence. The stochastic dependence considered, is modeled by a one-sided domino effect. The failure of component 1 at instant t may induce the failure of component 2 at instant t+τ with probability p _1→2. The time delay τ is a random variable with known probability density function h _{p 1→2} (.). The system is considered in a failed state when both components are failed. The proposed replacement policies suggest to replace the system upon failure or at age T whichever occurs first.

In the first policy, costs and durations associated with maintenance activities are supposed to be constant. In the second replacement policy, the preventive replacement cost depends on the system’s state and age. The expected cost per unit of time over an infinite span is derived and numerical examples are presented.

In this paper and especially in the second policy, the authors find that the authors can get a more economical policy if the authors consider that the preventive replacement cost is not constant but depends on T.

In this paper, the authors take into account of the stochastic dependence between system components. This dependence affects the global reliability of the system and replacement’s periodicity. It can be used to measure the performance of the system et introduced into design phase of the system.

To develop an integrated approach to forecasting spot foreign exchange rates by incorporating some principles underlying long‐term dependence.

The paper utilises the random‐walk framework to develop a stochastic forecast model wherein the sign (positive or negative) and magnitude (strong or weak) of dependence can be separately controlled. The integrated model demonstrates superior forecast performance over a conventional random walk.

Using spot log prices and log price changes (returns) for the USD/AUD exchange rate, the initial outcomes of the study suggest that a priori knowledge of the underlying sign and magnitude of long‐term dependence yields out‐of‐sample forecasts superior to those of a random walk model.

Independent assessment of the contribution to forecast accuracy of controlling for the sign of dependence between successive price changes only shows little additional improvement in out‐of‐sample forecast performance over the random walk null.

The findings of the study have important ramifications for managerial finance as they provide important insights on expected future currency returns with potential advantages in currency hedging and/or timing of international capital flows.

The contribution of this paper is to develop an original forecast model explicitly incorporating the conceptual and theoretical characteristics of long‐term dependent time series. By separating the key characteristics and modelling each individually, the contribution of each to forecast accuracy can be evaluated.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

The purpose of this paper is to propose a condition-based opportunistic maintenance policy considering economic dependence for a series–parallel hybrid system with a K-out-of-N redundant structure, where a single component in series is denoted as subsystem1, and K-out-of-N redundant structure is denoted as subsystem2.

Based on the theory of Residual Useful Life (RUL), inspection points are determined, and then different maintenance actions are adopted in the purpose of minimizing the cost rate. Both perfect and imperfect maintenance actions are carried out for subsystem1. More significantly, regarding economic dependence, condition-based opportunistic maintenance is designed for the series–parallel hybrid system: preemptive maintenance for subsystem1, and both preemptive and postponed maintenance for subsystem2.

The sensitivity analysis indicates that the proposed policy outperforms two classical maintenance policies, incurring the lowest total cost rate under the context of both heterogeneous and quasi-homogeneous K-out-of-N subsystems.

This model can be applied in series–parallel systems with redundant structures that are widely used in power transmission systems in electric power plants, manufacturing systems in textile factories and sewerage systems. Considering inconvenience and high cost incurred in the inspection of hybrid systems, this model helps production managers better maintain these systems.

In maintenance literature, much attention has been received in repairing strategies on hybrid systems with economic dependence considering preemptive maintenance. Limited work has considered postponed maintenance. However, this paper uses both condition-based preemptive and postponed maintenance on the issue of economic dependence bringing opportunities for grouping maintenance activities for a series–parallel hybrid system.

The traditional maintenance scheduling strategies of multi-component systems may result in maintenance shortage or overage, while system degradation information is often ignored. The purpose of this paper is to propose a multi-phase model that better integrates degradation information, dependencies and maintenance at the tactical level.

This paper proposes first a maintenance optimization model for multi-component systems with economic dependence and structural dependence. The cost of combining maintenance activities is lower than that of performing maintenance on components separately, and the downtime cost can be reduced by considering structural dependence. Degradation information and multiple maintenance actions within scheduling horizon are considered. Moreover, the maintenance resources can be integrated into the optimization model. Then, the optimization model adopting one maintenance activity is extended to multi-phase optimization model of the whole system lifetime by taking into account the cost and the expected number of downtime.

The superiority of the proposed method compared with periodic maintenance is demonstrated. Thus, the values of both integrated degradation information and considering dependencies are testified. The advantage of the proposed method is highlighted in the cases of high system utilization, long maintenance durations and low maintenance costs.

Few studies have been carried out to integrate decisions on degradation, dependencies and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic multi-phase model is proposed in this paper, along with an iterative solution algorithm for it.

The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into maintenance activities.

On the analysis of availability and maintenance cost, preventive maintenance (PM) models subjected to imperfect maintenance for different equipment types are built. And then, a cost-saving function of OM is established to find out an optimal OM strategy, depending on whether the front-bottleneck machines adopt OM strategy or not. A numerical example is given to show how the proposed bottleneck-based OM model proceeded.

The simulation results indicate that the proposed model is better than the methods to maintain the machines separately and the policy to maintain all machines when bottleneck machine reaches its reliability threshold. Furthermore, the relationship between OM strategy and corresponding parameters is identified through sensitivity analysis.

In practical situations, the bottleneck machine always determines the throughput of the whole series production system. Whenever a PM activity is carried out on the bottleneck machine, there will be an opportunity to maintenance other machines. Under such circumstances, findings of this paper can be utilized for the determination of optimal OM policy with the objective of minimizing total maintenance cost of the system.

This paper presents a bottleneck-based OM optimization model with the integration of the imperfect effect as a new method to schedule maintenance activities for a series production system with buffers. Furthermore, to the best of the knowledge, this paper presents the first attempt to considering the bottleneck constraint on system capacity and diverse types of machines as a means to minimize the maintenance cost and ensure the system throughput.

The purpose of this paper is to model a block-based inspection policy for a multi-component system with stochastic dependence. Some components may develop a hidden failure, an occurrence of which neither stops the system nor accelerates the other components’ deterioration. On the other hand, other components may experience three states: healthy, defective and revealed failures. Any revealed failure of each component not only stops the system but also generates a shock to all the other ones, which increases their occurrence rate of hidden, defect and revealed failures.

A block-based inspection policy is considered to take advantage of economic dependence as follows. In addition to the periodic inspections, the system is also inspected at revealed failures’ moments of each component to detect and fix both defects and hidden failures on all the other components. To calculate the expected total cost, the recursive equations for the required expected values is first mathematically derived. Then, due to computational complexity, an efficient Monte Carlo simulation algorithm is designed to calculate the expected values.

The proposed approach is illustrated through a numerical example, and the optimal periodic inspection interval over a finite time horizon is obtained via minimization of the expected total cost. Finally, the correctness of the results is validated by conducting sensitivity analysis.

Planning an appropriate inspection policy over a finite time horizon becomes more complicated when considering a multi-component system because different units may experience different failure modes with stochastic dependence.

The move toward Industry 4 is accelerated by the availability of affordable sensing technologies and networking infrastructure. Condition-Based Maintenance is the well-suited maintenance strategy to make use of the information available on assets condition to optimize maintenance interventions. However, devising the optimum maintenance policy requires a representative model of the maintenance system. Most of the existing research has been focusing on single-component systems. However, assets nowadays are complex and composed of many components. The modeling of multicomponent maintenance systems presents various challenges, especially if interactions between components, such as stochastic, structural, economic and resource dependencies are considered.

In this paper, we present a detailed modeling approach based on Discrete Event Simulation for nonidentical two-component systems subject to Condition-Based Maintenance considering all four types of dependencies.

The research has shown that optimizing the maintenance system without considering resource dependence led to different and better solutions. In addition, there is a trade-off between maintenance cost and asset availability, confirming the need for multiobjective optimization.

This paper outlines a modeling approach of CBM for nonidentical two-component systems considering stochastic, structural, economic and resource dependencies. A demonstration on a case study is followed where multiobjective optimization was applied to obtain the optimal maintenance policy while minimizing maintenance cost and maximizing asset availability simultaneously.

The purpose of this paper is to study the risk of misspecifying solvency models for insurance companies.

Based on a basic solvency model, the authors examine the sensitivity of different risk measures with respect to model misspecification. An analysis considers the effects of introducing stochastic jumps and linear, as well as non‐linear dependencies into the basic setting on the solvency capital requirements, shortfall probability and expected policyholder deficit. Additionally, the authors take a regulatory view and consider the degree to which the deviations in risk measures, due to the different model specifications, can be diminished by means of requiring interim financial reports.

The simulation results suggest that the sensitivity of solvency capital as a risk measure – as it is in regulatory practice – underestimates the actual misspecification risk that policyholders are exposed to. It is also found that semi‐annual mandatory interim reports can already reduce the model uncertainty faced by a regulator, significantly. This has important implications for the design of risk‐based capital standards and the implementation of internal solvency models.

The results from the Monte Carlo simulation show that changes in the specification of a solvency model have a much greater impact on shortfall probabilities and expected policyholder deficits than they have on capital requirements. The shortfall risk measures react much more sensitively to small changes in the model assumptions, than the capital requirements. This leads us to the conclusion that regulators should not solely rely on capital requirements to monitor the solvency situation of an insurer, but should additionally consider shortfall risk measures. More precisely, an analysis of model risk focusing on the sensitivity of capital requirements will typically underestimate the relevant risk of model misspecification from a policyholder's perspective. Finally, the simulation results suggest that mandatory interim reports on the solvency and financial situation of an insurance company are a powerful tool in order to reduce the model uncertainty faced by regulators.