This paper combines computer-based monitoring technologies and Internet of things (IoT) technology to develop IoT condition-based group replacement decision support system for a production/service system with numerous parallel independent operating servers. This proposed IoT conditioned-based group replacement decision support system first develops the discounted cost model for a service/production system with numerous independent working servers. The original discounted cost model is further revised into an equivalent model to stimulate the proof procedure by applying the uniformization approach. Several significant theoretical properties are proved and many numerical examples are conducted for two kinds of group replacement policies, respectively. The first class of group replacement policy is developed and proved theoretically that there is a threshold of amount of customers existed to activate the group replacement depending on various amount of operating servers; numerical examples conducted in this study can also illustrate the above theoretical outcomes already derived for the first class of group replacement policy. Besides, for the second class of group replacement policy, the results of numerical examples definitely demonstrate that there is a threshold of the amount of operating servers needed to start the group replacement according to distinct amount of customers in the system. This proposed IoT condition-based group replacement decision support system derives the structure and detailed procedure flow to actually conduct the group replacement operations for many practical service or production systems.
In this paper, we consider a cost model with periodic rejuvenation and derive the optimal software rejuvenation policy which minimizes the expected total discounted cost over an infinite time horizon, based on the familiar net present value approach. Further, since the failure time distribution can not be easily estimated from a few data samples in practice, we develop a statistically non-parametric algorithm to estimate the optimal periodic rejuvenation policy, provided that the complete sample data of failure times is given. Numerical examples based on the Monte Carlo simulation are illustrated to show asymptotic property of the proposed estimator.
Discounted Cost Model of Condition-Based Maintenance Regarding Cumulative Damage of Armor Units of Rubble-Mound Breakwaters as a Discrete-Time Stochastic Process
