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In this paper, we consider a component exhibiting a continuous monotone degradation modeled as a Gamma process. The maintenance policy is of condition‐based type with periodic inspections. It follows the two threshold control limit policy with a preventive maintenance limit, a switching limit, and a given failure limit. Our interest is to minimize the annual expected maintenance cost subject to a constraint on the average availability. The decision variables are the preventive limit, the switching limit, and the inspections schedule. We derive exact results for the key performance indicators, but since they are numerically involved, we propose a simple, that is, involving necessary spreadsheet calculations, accurate approximation. We apply our model to a case from the process industry, namely, a carbon steel hydrogen dryer consisting of a cylindrical pressure vessel. This vessel suffers from corrosion with thickness growth modeled as a Gamma process. The optimization results show that although the annual maintenance cost increases with the mean and the coefficient of variation of the yearly corrosion thickness, its mean has a higher impact on cost. Based on the cost sensitivity analysis, we find that the impact of a vast increase of the corrective maintenance cost for the same inspection and preventive costs can be mitigated by appropriately reducing the corrective maintenance probability. Finally, we empirically show that the two threshold policy achieves an annual cost savings up to 12.3%$12.3\%$ compared to the single threshold policy. [ABSTRACT FROM AUTHOR]

We divide customers into two types according to their purchasing behaviour: one is impatient and the other is patient. An impatient customer will be immediately lost if his reservation price of the product is below the current price, while a patient customer will wait until the price drops below his reservation price and buy one item. The seller’s objective is to maximise his expected discounted overall revenue by dynamically pricing. We prove that the optimal pricing policy has a control limit structure, examine the impact of the proportion parameter on the seller’s expected discounted overall revenue and show that the limiting behaviour of the optimal pricing policy when there are little patient customers is the same as the optimal static pricing policy. We also show that the seller will make more profit if he partially knows the number of waiting customers by providing subscription service to customers. A set of numerical results illustrates the value of dynamic pricing and information with respect to problem parameters such as the arriving probability and the subscription probability. [ABSTRACT FROM PUBLISHER]

Data admission control is an important issue in wireless sensor networks (WSNs) because of the limited transmission coverage area and the limited battery capability of each sensor node. To achieve the optimum benefit of the sensor network, a reward when a data package arriving at a sensor is accepted (not rejected) for transmission is considered, but a holding cost per unit time for the accepted data package waiting in the sensor for transmission is also incurred. For the sensor with sleep and active phases, a dynamic data admission control model is developed in this paper. By constructing a suitable Markov decision process (MDP), we verify that the optimal admission control policy of when to admit or reject an arriving data packet is a control limit policy, in order to achieve the maximum expected a discounted reward in the sleep and active phase, respectively. Furthermore, we provide a formula for how to calculate the upper bound of these threshold values. For the identified model with the optimal expected discount reward, the energy consumption of the sensors in active and sleep phases, as well as the energy consumption switching from active to sleep per unit time and vice versa is investigated. Extensive simulation is implemented. The results show that the problem is effectively solved by an optimal scheme with high energy efficiency. The results of this paper can be applied to designing optimal sensor nodes in wireless sensor networks.

Condition-based maintenance (CBM) is proved to be effective in reducing the long-run operational cost for a system subject to degradation failure. Most existing research on CBM focuses on single-unit systems where the whole system is treated as a black box. However, a system usually consists of a number of components and each component has its failure behavior. When degradation of the components is observable, CBM can be applied to the component level to improve the maintenance efficiency. This paper aims to study the optimal inspection/replacement CBM strategy for a multi-unit system. Degradation of each component is assumed to follow a Wiener process and periodic inspection is considered. We cast the problem into a Markov decision framework and derive the optimal maintenance decisions that minimize the maintenance cost. To better illustrate the optimal maintenance strategy, we start from a 1-out-of-2: G system and show that the optimal maintenance policy is a two-dimensional control limit policy. The argument used in the 1-out-of-2: G system can be readily extended to general cases in a similar way. The value iteration algorithm is used to find the optimal control limits, and the optimal inspection interval is subsequently determined through a one-dimensional search. A numerical study and a comprehensive sensitivity analysis are provided to illustrate the optimal maintenance strategy. [ABSTRACT FROM PUBLISHER]

We review the literature on individual patient organ acceptance decision making by presenting a Markov Decision Process (MDP) model to formulate the organ acceptance decision process as a stochastic control problem. Under the umbrella of the MDP framework, we classify and summarize the major research streams and contributions. In particular, we focus on control limit-type policies, which are shown to be optimal under certain conditions and easy to implement in practice. Finally, we briefly discuss open problems and directions for future research. [ABSTRACT FROM AUTHOR]

Presently, hard disks have become indispensable elements in most computer systems. If any malfunction occurs in the hard disk, valuable data can be lost; malfunctions such as sector failures can be caused by defects of the magnetic materials on the recording medium. When a sector failure occurs, the data in the failed sector are lost. However, the entire function can be restored to a normal condition by providing a process of replacement of the subject sector into a spare sector in advance. Since there is an upper limit to the number of spare sectors, a general failure can occur if the total number of sector failures exceeds the upper limit. Alternatively, an unstable state occurs in which the restoration of the information is not possible even though the entire structure appears to be working. It is rare that sector failure is discovered immediately. Instead, it is discovered when the hard disk is inspected. It is therefore possible to determine the total number of sector failures by periodic inspection and to avoid general failures and the extremely unstable states due to sector failure by preventive replacement. For the above situations, this paper proposes a control limit policy for the inspection of sector failure and verifies its effectiveness. © 1999 Scripta Technica, Electron Comm Jpn Pt 1, 82(10): 18–24, 1999 [ABSTRACT FROM AUTHOR]