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Most systems deteriorate with age and use, and eventually, fail from either or both causes in random environment. It is reasonable in commercial industries that only one spare item is available for...

This paper proposes and analyses the generalized sequential preventive maintenance policies for an operating system that works at random processing times and subject to shocks. The shocks arrive ac...

This paper investigates the optimal preventive replacement policy for products with random work under a renewing free-replacement warranty (RFRW). To model an imperfect maintenance action when the ...

A viable wide-band circuit model of parallel corrugated differential transmission line is established. By solving for the even- and odd-modes in this structured differential transmission line, the equivalent capacitance, inductance, resistance, and conductance per unit length are calculated, then the characteristic impedances of differential and common signals are obtained. The ${S}$ -parameters obtained from the equivalent circuit model agree well with the full-wave simulation results with a deviation of only about 0.036 dB in the frequency range of 10 GHz. Experimentally, the characteristic impedances are measured for the differential and common signals using a time domain reflectometer (TDR). The deviation between the experimental and theoretical results was less than 1.05%. Based on the equivalent circuit model, it is feasible to implement this type of structured differential line directly into real circuits.

This article addresses a multi-parameter preventive replacement policy for a stochastically deteriorating system with random working times and imperfect maintenance. The system is subject to two ty...

This paper presents a preventive replacement problem when a system is operating successive works with random times and suffering stochastic shocks. The works cause random amount additive damage to ...

System maintenance and spare parts are two closely related logistics activities since maintenance generates the demand for spare parts. Most studies on integrated models of preventive replacement a...

In the era of big data how to precisely and efficiently recommend the items that a user really needs is an ongoing research topic. Collaborative filtering based on similarity measure is one of common approaches to achieve this purpose. But similarity comparison becomes not so efficient when the amount of data becomes vast. In this paper we propose a recommendation mechanism which goes through multiple stages of filtering based on user's preferences. We differentiate positive and negative preferences and identify similarity based on them. The users for further collaborative filtering can be reduced by excluding those under the designated threshold. Furthermore, we can set different weights for positive and negative preferences to accommodate different application fields. The experiments show our method can reduce the processing time when there is a large volume of data without loss of the recommendation quality.

This paper proposes preventive replacement policies for an operating system which may continuously works for N jobs with random working times and is imperfectly maintained upon failure. As a failure occurs, the system suffers one of the two types of failures based on some random mechanism: type-I (repairable or minor) failure is rectified by a minimal repair, or type-II (non repairable or catastrophic) failure is removed by a corrective replacement. A notation of preventive replacement last model is considered in which the system is replaced before any type-II failure at an operating time T or at number N of working times, whichever occurs last. Comparisons between such a preventive replacement last and the conventional replacement first are discussed in detail. For each model, the optimal schedule of preventive replacement that minimizes the mean cost rate is presented theoretically and determined numerically. Because the framework and analysis are general, the proposed models extend several exis...

This paper considers an operating system subject to shocks occurring as a non-homogeneous pure birth process (NHPBP). A shock can cause two types of system failures: a type-I failure (minor failure) is rectified by a general repair, whereas a type-II failure (catastrophic failure) is removed by an unplanned replacement or remains inactive until the next planned replacement. The probabilities of these two types of failures depend on the number of shocks since the last replacement. We consider a modified block replacement policy (BRP) policy with a random repair cost as follows: first, an operating system is preventively replaced with a new one at times iT ( I = 1,2, …) regardless its failure history. If the system fails in [( i − 1) T , ( i − 1) T + T 0 ), it is either replaced by a new one or generally repaired, and if fails in [( i − 1) T + T 0 , iT ), it is either generally repair (minor failure) or remains inactive (catastrophic failure) until the next planned replacement. Thus the policy is called a two variable policy with a fixed replacement interval and a threshold for the system age. The expected cost rate is derived by using the renewal reward theory. The two-variable cost function is transformed into a one variable function and the optimal policy is analyzed.

This paper studies the optimal preventive maintenance (PM) policies for multi-state systems. The scheduled PMs can be either imperfect or perfect type. The improved effective age is utilized to model the effect of an imperfect PM. The system is considered as in a failure state (unacceptable state) once its performance level falls below a given customer demand level. If the system fails before a scheduled PM, it is repaired and becomes operational again. We consider three types of major, minimal, and imperfect repair actions, respectively. The deterioration of the system is assumed to follow a non-homogeneous continuous time Markov process (NHCTMP) with finite state space. A recursive approach is proposed to efficiently compute the time-dependent distribution of the multi-state system. For each repair type, we find the optimal PM schedule that minimizes the average cost rate. The main implication of our results is that in determining the optimal scheduled PM, choosing the right repair type will significantly improve the efficiency of the system maintenance. Thus PM and repair decisions must be made jointly to achieve the best performance.

We propose an electrostatics-based placement algorithm for large-scale mixed-size circuits (ePlace-MS). ePlace-MS is generalized, flat, analytic and nonlinear. The density modeling method eDensity is extended to handle the mixed-size placement. We conduct detailed analysis on the correctness of the gradient formulation and the numerical solution, as well as the rationale of dc removal and the advantages over prior density functions. Nesterov’s method is used as the nonlinear solver, which shows high yet stable performance over mixed-size circuits. The steplength is set as the inverse of Lipschitz constant of the gradient function, while we develop a backtracking method to prevent overestimation. An approximated nonlinear preconditioner is developed to minimize the topological and physical differences between large macros and standard cells. Besides, we devise a simulated annealer to legalize the layout of macros and use a second-phase global placement to reoptimize the standard cell layout. All the above innovations are integrated into our mixed-size placement prototype ePlace-MS, which outperforms all the related works in literature with better quality and efficiency. Compared to the leading-edge mixed-size placer NTUplace3, ePlace-MS produces up to 22.98% and on average 8.22% shorter wirelength over all the 16 modern mixed-size benchmark circuits with the same runtime.