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This paper presents approximation methods for the performance analysis of long manufacturing lines, i.e. lines with more than two machines and one buffer, that have both quality and operational failures. We describe three different versions of long flow lines that differ in the locations of the inspection stations and in the sets of machines that each inspection station monitors. We explain a transformation method that approximates long manufacturing lines that have quality and operational failures with long lines that only have operational failures. Such lines can be evaluated by decomposition methods. We introduce other approximations to quantify the effects of the separation of inspections from operations. Comparison with simulation shows that the solution methods provide reliable performance estimates. [ABSTRACT FROM AUTHOR]

This paper describes an approximate analytical method for evaluating the average values of throughput and buffer levels of closed three-machine production systems with finite buffers. The method includes a new set of decomposition equations and a new building block model. The machines have deterministic processing times and geometrically distributed probabilities of failure and repair. The numerical results of the method are close to those from simulation. The method performs well because it takes into account the correlation among the numbers of parts in the buffers. Extensions to larger systems are discussed. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix of additional numerical results] [ABSTRACT FROM AUTHOR]

Process capability indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. Properties of the univariate processes have been investigated extensively, but are comparatively neglected for multivariate processes where multiple dependent characteristics are involved in quality measurement. In this paper, we consider two commonly used multivariate capability indices MCp and MCpm, to evaluate multivariate process capability. We investigate the statistical properties of the estimated MCp and obtain the lower confidence bound for MCp. We also consider testing MCp, and provide critical values for testing if a multivariate process meets the preset capability requirement. In addition, an approximate confidence interval for MCpm is derived. A simulation study is conducted to ascertain the accuracy of the approximation. Three examples are presented to illustrate the applicability of the obtained results. [ABSTRACT FROM AUTHOR]

Analytical models of multi-product manufacturing systems operating under CONWIP control are composed of closed queuing networks with synchronization stations. Under general assumptions, these queuing networks are hard to analyze exactly and therefore approximation methods must be used for performance evaluation. This research proposes a new approach based on parametric decomposition. Two-moment approximations are used to estimate the performance measures at individual stations. Subsequently, the traffic process parameters at the different stations are linked using stochastic transformation equations. The resulting set of non-linear equations is solved using an iterative algorithm to obtain estimates of key performance measures such as throughput, and mean queue lengths. Numerical studies indicate that the proposed method is computationally efficient and yields fairly accurate results when compared to simulation. [ABSTRACT FROM AUTHOR]

In this paper an analytical approximation for the performance of non-homogenous asynchronous Flow Production Systems (FPSs) with finite buffers is presented. Generally distributed stochastic processing times as well as breakdowns and imperfect production are considered. The procedure explicitly accounts for simultaneous blocking and starving. The approximation is based on the decomposition of an M-station-line into M -- 1 two-station-lines which are analyzed with the help of a GI/G/ I /Nmax queueing model. Numerical comparisons with exact and simulation results for hypothetical as well as for real-life flow-lines indicate that the procedure provides accurate results. [ABSTRACT FROM AUTHOR]

Considerable studies have been done on modelling and joint monitoring of input and output of systems with autoregressive moving average (ARMA) disturbance. Most of these studies focus on systems with ARMA (1, 1) disturbance. However, many kinds of systems are not conform to ARMA(1, 1) disturbance. Motivated by the fact that an autoregressive (AR) model with high order can be implemented to approximate the stationary ARMA model at any precision, a new generic model and a joint monitoring scheme of systems with arbitrary order AR(p) disturbance are developed. A minimum mean squared error (MMSE) controller with arbitrary order AR disturbance is designed to reduce the system variability. The mathematical expectation and average run length of MMSE-controlled outputs are derived. A new joint chart for monitoring the input and output simultaneously is explored. Two out-of-control rules for the joint monitoring chart are developed. The monitoring performances of the input chart, the output chart and the joint monitoring chart are also discussed. The results of simulation experiments and case studies validate the effectiveness of the developed model and joint monitoring chart. [ABSTRACT FROM PUBLISHER]