http://null TOC Alert for Publication# 24 2013 May 20 62 1 C1 1 131 62 1 C2 C2 132 s-independent Type-II right censored data. In particular, we consider parametric inference using Best Linear Unbiased Estimation, as well as non-parametric inference. We provide pertinent numerical results and two examples to illustrate all the methods of inference developed here.]]> 62 1 2 12 2333 62 1 13 22 936 62 1 23 36 1497 62 1 37 48 2298 62 1 49 65 3124 t=0). However, due to the large uncertainty in the system's performance through life, an optimal maintenance policy requires both permanent monitoring and a cost-efficient plan of interventions. This paper presents a model to define an optimal maintenance policy of systems that deteriorate as a result of shocks. Deterioration caused by shocks is modeled as a compound Poisson process, and the optimal maintenance strategy is based on an impulse control model. In the model, the optimal time and size of interventions are executed according to the system state, which is obtained from permanent monitoring.]]> 62 1 66 72 1321 n th type-I failure, or at any type-II failure. The aim of this paper is to determine the optimal policy n*, the number of minor failures up to replacement that minimizes the expected cost rate of the system subject to NHPBP shocks. The model is a generalization of the existing models, and is more applicable in practice. We present some numerical examples, and show that several classical models are the special cases of our model.]]> 62 1 73 81 2826 62 1 82 91 1306 62 1 92 104 2793 s-t flow between a pre-specified source node s and a pre-specified terminal node t in the network. The problem of determining the expected value of maximum s-t flow is computationally hard. Therefore, for practical sized networks, it is estimated through Monte Carlo based simulation methods which estimate the measure by evaluating the maximum flows in a large sample of network states. Such methods are computationally expensive. In this paper, we present an algorithm which speeds up the process of evaluating maximum flows in the sampled states by a factor of three on two difficult classes of randomly generated networks. This speed-up allows us to compute the measure for larger networks than is currently possible. It also allows us to obtain more accurate estimates on similar sized problems within similar execution times.]]> 62 1 105 115 2152 62 1 116 126 1327 62 1 127 135 1284 62 1 136 145 811 62 1 146 159 2658 62 1 160 170 1303 62 1 171 182 2255 62 1 183 198 4143 62 1 199 210 1336 62 1 211 225 2582 62 1 226 237 1502 62 1 238 243 581 62 1 244 255 1806 s-independent. Failures occur randomly, and are handled by several repair teams. If a repair team is available when a failure occurs, then the repair (or replacement) is started immediately; otherwise the component waits in the repair queue. It is assumed that each component's time to failure is exponentially distributed, and the failure intensity depends on the other components' states. No assumption is made about the components' repair time distributions. The model's complexity makes it impossible to analytically compute the parameters of the system's failure-repair process. For this reason, the sought parameters are evaluated using a combination of Monte Carlo simulation and statistical estimation. Finding the confidence interval is a non-trivial task. The first part of the paper is theoretical; the conditions under which the failure-repair process is recurrent are given, and the confidence intervals for the sought parameters are defined. The second part has an applicable character; a commodity transport network is considered as an exemplary system with inter-component dependencies, and the algorithm estimating its reliability parameters is presented with some illustrative examples.]]> 62 1 256 266 2091 n linearly ordered multistate components. Each component can have different states: from complete failure, up to perfect functioning. A performance rate is associated with each state. The system fails if in each of at least m consecutive overlapping groups of r consecutive components (windows) the sum of the performance rates of components belonging to the group is lower than a minimum allowable level. It is shown that, in the case of different components, the system reliability depends on their arrangement. The optimal arrangement problem is formulated, and a numerical tool for solving this problem is suggested. The tool uses an extended universal moment generating function technique for system reliability evaluation, and a genetic algorithm for optimization. Examples of system reliability optimization are presented.]]> 62 1 267 275 2040 n elements each having two s -dependent subcomponents. The reliability of such systems involves the distributions of bivariate order statistics, and are connected with a bivariate binomial distribution. The mean residual life function of complex systems with intact components at time t is also discussed. Some examples and graphical representations are given.]]> 62 1 276 285 2696 62 1 286 295 1855 CL is used to measure the larger-the-better type quality characteristics. Accelerated life test (ALT) has often been used to yield information quickly so that the life distribution of products can be estimated. This study constructs a maximum likelihood estimator (MLE) of CL for exponential products based on type II right censored data from the step-stress accelerated life test (SSALT). The MLE of CL is then utilized to develop the hypothesis testing procedure with the given lower specification limit L . This new testing procedure can be easily applied to assess whether the lifetime of products meets the requirements. Finally, we give two examples to explicate the proposed testing procedures.]]> 62 1 296 304 1880 62 1 305 311 1014 62 1 312 312 630 62 1 313 313 283 62 1 314 314 1155 62 1 315 315 1371 62 1 316 316 320 62 1 C3 C3 1054 62 1 C4 C4 1108