Applied Stochastic Models in Business and Industry最新文献

We consider a Markovian queueing model with abandonment where customer arrival, service and abandonment processes are all modulated by an external environmental process. The environmental process depicts all factors that affect the exponential arrival, service, and abandonment rates. Moreover, the environmental process is a hidden Markov process whose true state is not observable. Instead, our observations consist only of customer arrival, service, and departure times during some period of time. The main objective is to conduct Bayesian analysis in order to infer the parameters of the stochastic system, as well as some important queueing performance measures. This also includes the unknown dimension of the environmental process. We illustrate the implementation of our model and the Bayesian approach by using simulated and actual data on call centers.

We consider an optimal stochastic control problem for a dam. Electrical power production is operating under an uncertain setting for electricity market prices and water level which has to be kept under control. Indeed, the water level inside the basin cannot exceed a certain threshold for safety reasons, and at the same time cannot decrease below another threshold in order to keep power production active. We model this situation as a mixed control problem with regular and switching controls under constraints. We characterize the value function as solution of an HJB equation and provide some numerical approximating methods. We shall illustrate by numerical examples the main achievements of the present approach.

This paper addresses and acknowledges the valuable feedback provided by Dr. Deniz Preil in response to the recent study conducted by Kurian et al which investigates the application of proximal policy optimization (PPO) to determine dynamic ordering policies within multi-echelon supply chains. The first comment raised by Dr. Preil motivated an examination of the training and evaluation procedures in Experiments 2, 3, and 4. The Experiments 2 and 3 were reworked to address this, allowing the seed to vary for every training iteration, resulting in refined outcomes while there was no need of reworking of Experiment 4. The second comment focused on the benchmarking strategies involving the 1-1 policy and the order-up-to (OUT) policy, clarifying the distinctions between the two policies and justifying the use of the 1-1 policy for benchmarking in Experiment 4. The implementation of the widely accepted OUT policy was explained, highlighting the meaningful rationale behind its use. These discussions aim to enhance the methodology employed by Kurian et al and strengthen the implications of the findings within the domain of supply chain ordering management.

This article corrects the following:

In this research paper by Chew et al.,¹ on page 590, the funding information in the Acknowledgement is incorrect.

The correct funding information should be:

This work is funded by the Ministry of Higher Education Malaysia, Fundamental Research Grant Scheme [Grant Number: FRGS/1/2019/STG06/USM/02/5], for the project entitled “New Robust Adaptive Model for Coefficient of Variation in Infinite and Finite Horizon Processes.”

We apologise for this error.

We response to comments on our paper “Specifying Prior Distributions in Reliability Applications” in this rejoinder.

The ability to predict failures in hard disk drives (HDDs) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss, improve service reliability, and reduce data center downtime. Most HDDs are equipped with a threshold-based monitoring system named self-monitoring, analysis and reporting technology (SMART). The system collects several performance metrics, called SMART attributes, and detects anomalies that may indicate incipient failures. SMART works as a nascent failure detection method and does not estimate the HDDs' remaining useful life. We define critical attributes and critical states for hard drives using SMART attributes and fit multi-state models to the resulting semi-competing risks data. The multi-state models provide a coherent and novel way to model the failure time of a hard drive and allow us to examine the impact of critical attributes on the failure time of a hard drive. We derive dynamic predictions of conditional survival probabilities, which are adaptive to the state of the drive. Using a dataset of HDDs equipped with SMART, we find that drives are more likely to fail after entering critical states. We evaluate the predictive accuracy of the proposed models with a case study of HDDs equipped with SMART, using the time-dependent area under the receiver operating characteristic curve (AUC) and the expected prediction error (PE). The results suggest that accounting for changes in the critical attributes improves the accuracy of dynamic predictions.

A system of $��n�$ components is here considered, with component deterioration modeled by non decreasing time-scaled Lévy processes. When a component fails, a sudden change in the time-scaling functions of the surviving components is induced, which makes the components stochastically dependent. We compute the reliability function of coherent systems under this new dependence model. We next study the distribution of the ordered failure times, and establish some positive dependence properties. We also provide stochastic comparison results in the usual multivariate stochastic order between failure times of two dependence models with different parameters. Finally, some numerical experiments illustrate the theoretical results.

We propose a real options model with regime shifts to investigate the effect of cutting interest rates on corporate investments when a financial crisis occurs. Cutting interest rates would lower the investment project's hurdle rate. The reduction in hurdle rate is positively related to the magnitude of interest rate cuts and the persistence of the financial crisis. The hurdle rate becomes lower in the financial crisis state because the reduction in interest rate would lower the cost of capital and the opportunity cost of immediate investment. In the numerical analysis of this study, we show that the change in the opportunity cost accounts for most of the change in the hurdle rate. Upon taking into consideration the firm's financing constraints, we find that cutting interest rates accelerates investments for firms with high liquidity. However, for firms with low liquidity, the optimal investment threshold is not affected by the variation in interest rates. Instead, the investments of low-liquidity firms are affected by the change in the friction of credit supply.