In the current paper, we study the concept of dynamic scheduling in flexible flow Shop with limited carrier under stochastic machine breakdowns and processing time. In most flexible flow shop scheduling methods reported in the literature consider a determinist environment, where all data of the problem are known in advance. Most of these works focused on the development of methods to produce a ...

We present a new technique for fast computation of the route that maximizes the probability of on-time arrival in stochastic networks, also known as the path-based stochastic on-time arrival (SOTA) problem. We utilize the solution to the policy-based SOTA problem, which is of pseudopolynomial time complexity in the time budget of the journey, as a heuristic for efficiently computing the optimal...

Many seasonal macroeconomic time series are subject to changes in their means and variances over a long time horizon. In this paper we propose a general treatment for the modelling of time-varying features in economic time series. We show that time series models with mean and variance functions depending on dynamic stochastic processes can be sufficiently robust against changes in their dynamic...

Given a stochastic process Xt, t b T CR, and s ~ R, then a) iff b): a) For every probability measure p on there is a stopping time T for Xt with law L(T) = p: b) If At is the smallest a-algebra for which Xu are measurable for all u ~ t, then P restricted to At is nonatomic for all t > s. This note began with a question of G. Shiryaev, connected with the following example. Let Wt be a standard W...

A fundamental problem in statistics is to develop models based on a sample of observations and drawing inferences using the model so developed. In growth modeling, data are usually collected over time. One characteristic of such data is that the successive observations are dependent. Each observation of the observed data series Yt may be considered as a realization of a stochastic process {Yt},...

We establish weak convergence of partial sums of tempered fractional time series (TFTS) to a stochastic process which we call a tempered Gaussian Hermite process (TGHP). We also introduce the Wiener integral with respect to TGHP, and establish weak convergence of weighted sums of TFTS to this Wiener integral.

One of the earliest results in flow shop scheduling theory is an algorithm given by Johnson’s [1] for scheduling jobs on two or three machines to minimize the total elapsed time whenever the processing times of jobs are random. The present paper is an attempt to develop a heuristic algorithm for two stages specially structured flow shop scheduling in which the processing times of the jobs a...

We consider a class of stochastic processing networks. Assume that the networks satisfy a complete resource pooling condition. We prove that each maximum pressure policy asymptotically minimizes the workload process in a stochastic processing network in heavy traffic. We also show that, under each quadratic holding cost structure, there is a maximum pressure policy that asymptotically minimizes...

This paper presents a method of fault prognostics and reliability estimation for DC motor using time series modeling procedure based on DC motor performance degradation data. DC motor performance degradation data are treated as a time series data and stochastic process are utilized to describe the degradation process for predicting long-term trend. A degradation test is processed for DC motor u...