The complexity of dependence between different types of components results in many challenges to estimate system reliability and to optimize maintenance plans. In this paper, we develop a reliability model to study the failure dependence of a system with a main component and several protective auxiliary components. Damage to the main component caused by random environmental shocks depends on the number

Although online business has been growing for some time, third-party e-platforms and their impact on e-channels are an under-explored area in the literature on dual-channel supply chains. Considering different combinations of open and self-support e-platform, this paper develops dynamic game models in four dual-channel e-retail structures to study pricing strategies and channel preference for manufacturers

This paper studies a healthcare system under two priority charging schemes. The first is a fee-for-priority (FFP) scheme under which the patients can choose to buy the priorities or not at their admission or readmission to the healthcare provider (HCP). The second is a bundled priority (BP) scheme under which the HCP receives a lump sum priority payment from patients for the entire episode of priority

Organizations have successfully used dynamic pricing to optimize revenues for many years, where research and practice have mainly focused on applications with independent, discrete commodities; for example, an airline ticket. In this research we consider applications where the commodity is continuous and the value of the commodity available to sell depends on the combination of previously accepted

The modelling of supply chain risk management (SCRM) has attracted increasing attention from researchers and professionals. However, a systematic network analysis of the literature to understand the development of research over time is lacking. Therefore, this study reviews SCRM modelling and its evolution as a scientific field. We collected 566 papers published in the Scopus database and shortlisted

This is the special issue for the Third International Symposium on Stochastic Models in Reliability Engineering, Life Sciences and Operations Management held in Beijing, 28–31 May 2019. The symposium provides a forum for researchers and practitioners in their respective fields of expertise to exchange new ideas and share the latest results on stochastic modelling in reliability engineering, life science

This paper considers sports matches in which two teams compete to score more points within a set amount of time (e.g. football, ice hockey). We focus on the order in which the competing teams score during the match (order of scoring). This type of order of scoring problem has not been addressed previously, and doing so here gives new insights into sports matches. For example, our analysis can deal

In quantitative finance practice, model calibration is a key challenge. This is especially so when a local regime-switching model needs to be calibrated because designing an efficient and reliable algorithm to obtain local volatility values as a function of underlying price and time is important for the model to be successfully used in practice. Therefore, this paper proposes a new algorithm for calibrating

This work proposes a novel framework for planning the capacity of diagnostic tests in cancer pathways that considers the aggregate demand of referrals from multiple cancer specialties (sites). The framework includes an analytic tool that recursively assesses the overall daily demand for each diagnostic test and considers general distributions for both the incoming cancer referrals and the number of

In this paper, we construct quantitative models in which the dependence structure of the firms’ default times is incorporated. Such models serve as the underlying frameworks in our proposed approach to price and hedge basket credit derivatives. Through the Gaussian copula-based method, we model the default correlation risk and develop valuation formulas for credit derivatives. Using single-name derivatives

We propose quantum majorization as a way of comparing and ranking correlation matrices, with the aim of assessing portfolio risk in a unified framework. Quantum majorization is a partial order in the space of correlation matrices, which are evaluated through their spectra. We discuss the connections between quantum majorization and an important class of risk functionals, and we define two new risk

Decision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the

We study a model of a two-stage degradation process in a dynamic environment. The two stages, the normal stage and the defective stage, are separated by the first hitting time of the alarm threshold by the degradation level. Wiener processes with piecewise linear drift are used in each stage to describe the degradation level in a dynamic environment. System failure is triggered in two ways: the system

The conventional inverse data envelopment analysis (DEA) model is only applicable to positive data, while negative data are commonly present in most real-world applications. This paper proposes a novel inverse DEA model that can handle negative data. The conventional inverse DEA model is a special case of our model as our model is more general in terms of returns-to-scale properties. The proposed model

It is common for firms to forecast stationary demand using simple exponential smoothing (SES) due to the ease of computation and understanding of the methodology. We consider a retailer who observes autoregressive moving average (ARMA) demand but for the sake of convenience, uses the widely available SES method to forecast its demand. This creates a potential disconnect between the true mechanism generating

In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single

We develop a mathematical model to study optimal preventive maintenance (PM) strategy under a two-dimensional stair-case warranty policy considering both age and usage of a product. During the warranty term, sellers implement preventive maintenance within the stair-case region of the warranty policy and upon failure a product is repaired minimally and immediately. Our objective is to find the optimal

This paper models age replacement and block replacement when there is the possibility of defaulting on the planned maintenance. A default occurs when a planned preventive replacement is not executed, and we discuss how defaults can arise in practice. Our aim is to study the robustness of block replacement and age replacement, bearing in mind that (a) these policies are frequently used in practice,

Forecasting non-stationary time series, especially when the data generating processes contains a random walk component, is a difficult and sometimes impossible task. In this paper we suggest an intuitive, computationally fast and expedient way of forecasting time series of the above type using distance-based nearest neighbours (NN). We exploit to advantage the path and scale dependence present in a

A new model of hybrid preventive maintenance of systems with partially observable degradation is developed. This model combines condition-based maintenance with age replacement maintenance in the proposed, specific way. A system, subject to a shock process, is replaced on failure or at some time |${T}_S$| if the number of shocks experienced by this time is greater than or equal to m or at time |$T>{T}_S$|

The interaction between nurses and their managers is a very important factor in nurses’ error reporting behaviour, which is crucial to improving patient safety in healthcare organizations. However, little theoretical work has been undertaken to analyse this interaction. This paper uses a game-theoretic principal–agent framework with asymmetric information to study this interaction. We suppose that

In this paper, the fair price of an American-style resettable convertible bond (CB) under the Black–Scholes model with a particular reset clause is calculated. This is a challenging problem because an unknown optimal conversion price needs to be determined together with the bond price. There is also an additional complexity that the value of the conversion ratio will change when the underlying price

Inverse (DEA) is an approach to estimate the expected input/output variation levels when the efficiency score reminds unchanged. Essentially, finding most efficient decision-making units (DMUs) or ranking units is an important problem in DEA. A new ranking system for ordering extreme efficient units based on inverse DEA is introduced in this article. In the adopted method, here the amount of required

This paper proposes a regression-based simulation algorithm for multi-period mean-variance portfolio optimization problems with constraints under a high-dimensional setting. For a high-dimensional portfolio, the least squares Monte Carlo algorithm for portfolio optimization can perform less satisfactorily with finite sample paths due to the estimation error from the ordinary least squares (OLS) in