Asymptotic variance rate of the output of a transfer line with no buffer storage and cycle-dependent failures

In this study the variability properties of the output of transfer lines are investigated. The asymptotic variance rate of the output of an N-station synchronous transfer line with no interstation buffers and cycle-dependent failures is analytically determined. Unlike the other studies, the analytical method presented in this study yields a closed-form expression for the asymptotic variance rate of the output. The method is based on a general result derived for irreducible recurrent Markov chains. Namely, the limiting variance of the number of visits to a state of an irreducible recurrent Markov chain is obtained from the n-step transition probability function. Thus, the same method can be used in other applications where the limiting variance of the number of visits to a state of an irreducible recurrent Markov chain is of interest. Numerical results show that the asymptotic variance rate of the output does not monotonically increase as the number of stations in the transfer line increases. The asymptotic variance rate of the output may first increase and then decrease depending on the station parameters. This property of the production rate is investigated through numerical experiments and the results are presented.     

Multi-state throughput analysis of a two-stage manufacturing system with parallel unreliable machines and a finite buffer

This paper models and analyzes the throughput of a two-stage manufacturing system with multiple independent unreliable machines at each stage and one finite-sized buffer between the stages. The machines follow exponential operation, failure, and repair processes. Most of the literature uses binary random variables to model unreliable machines in transfer lines and other production lines. This paper first illustrates the importance of using more than two states to model parallel unreliable machines because of their independent and asynchronous operations in the parallel system. The system balance equations are then formulated based on a set of new notations of vector manipulations, and are transformed into a matrix form fitting the properties of the Quasi-Birth–Death (QBD) process. The Matrix-Analytic (MA) method for solving the generic QBD processes is used to calculate the system state probability and throughput. Numerical cases demonstrate that solution method is fast and accurate in analyzing parallel manufacturing systems, and thus prove the applicability of the new model and the effectiveness of the MA-based method. Such multi-state models and their solution techniques can be used as a building block for analyzing larger, more complex manufacturing systems.     

Analysis of a finite buffer model with two servers and two nonpreemptive priority classes

In this paper, we analyze a finite buffer queueing model with two servers and two nonpreemptive priority service classes. The arrival streams are independent Poisson processes, and the service times of the two classes are exponentially distributed with different means. One of the two servers is reserved exclusively for one class with high priority and the other server serves the two classes according to a nonpreemptive priority service schedule. For the model, we describe its dynamic behavior by a four-dimensional continuous-time Markov process. Applying recursive approaches we present the explicit representation for the steady-state distribution of this Markov process. Then, we calculate the Laplace–Stieltjes Transform and the steady-state distribution of the actual waiting times of two classes of customers. We also give some numerical comparison results with other queueing models.     

Simulation optimization of buffer allocations in production lines with unreliable machines

We use a recent simulationbased optimization method, sample path optimization, to find optimal buffer allocations in tandem production lines where machines are subject to random breakdowns and repairs, and the product is fluidtype. We explore some of the functional properties of throughput of such systems and exploit these properties to prove the almost sure convergence of our optimization technique, under a regularity condition on the steady state. Utilizing a generalized semiMarkov process (GSMP) representation of the system, we derive recursive expressions to compute onesided directional derivatives of throughput, from a single simulation run. Finally, we give computational results for lines with up to 50 machines. We also compare results for smaller lines with the results from a more conventional method, stochastic approximation, whenever applicable. In these numerical studies, our method performed quite well on problems that are considered difficult by current computational standards.     

Optimal maintenance of two stochastically deteriorating machines with an intermediate buffer

We consider a manufacturing system in which an input generating installation transfers a raw material to a subsequent production unit. Both machines deteriorate stochastically with usage and may fail. For each machine the deteriorating process is described by some known transition probabilities between different degrees of deterioration. A buffer has been built between the two machines in order to cope with unexpected failures of the installation. A discrete-time Markov decision model is formulated for the optimal preventive maintenance of both machines. The maintenance times are geometrically distributed and the cost structure includes operating costs, storage costs, maintenance costs and costs due to the lost production. It is proved that for fixed buffer content and for fixed deterioration degree of one machine, the average-cost optimal policy initiates a preventive maintenance of the other machine if and only if its degree of deterioration exceeds some critical level. We study, by means of numerical results, the effect of the variation of some parameters on the optimal policy and on the minimum average cost. For the case in which the maintenance times follow continuous distributions, an approximate discrete-time Markov decision model is proposed.     

Optimal transfer policy for a production line with learning curve

Summary A multi-stage production line is to be transferred to a new location. The production process is subject to a sensitive learning curve. The learning process after transfer must be started from scratch. For this reason abrupt transfer of the line would call for substantial additional capacity and cost. This paper is concerned with finding the cost-minimizing transfer policy.

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Zusammenfassung Eine mehrstufige Produktionslinie soll in ein neues Werk verlegt werden. Der Produktionsprozeß unterliegt einer empfindlichen Lernkurve, die bei Verlegung von vorn durchlaufen werden muß. Aus diesem Grunde würde eine abrupte Verlegung erhebliche Mehrkapazität und damit Mehrkosten erfordern. Die vorliegende Arbeit beschäftigt sich mit der Auffindung der kostenoptimalen Verlegungsweise.

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Vorgel. v.:H. Albach     

A queueing model of a production system with two machines,one operator and priorities

This paper studies a priority queueing model of a production system in which one operator serves two types of units with overlapping service times. The two types of units arrive in independent Poisson processes. There are two machines in the system. Units of type 1 receive two consecutive types of services at machine #1: the handwork performed by the operator and the automatic machining without the operator. Units of type 2 receive only the handwork performed by the operator at machine #2. The operator attends the two machines according to a strict-priority discipline which always gives units of type 2 higher priority than units of type 1. At each machine the handwork times have a general distribution, and at machine #1 the machining times have an exponential distribution. The Laplace-Stieltjes transform of the queue-size distributions and the waiting time distributions for a stationary process are obtained.     

Design of a production system with a feedback buffer

In this paper, we deal with an M/G/1 Bernoulli feedback queue and apply it to the design of a production system. New arrivals enter a “main queue” before processing. Processed items leave the system with probability 1-p or are fed back with probability p into an intermediate finite “feedback queue”. As soon as the feedback queue is fully occupied, the items in the feedback queue are released, all at a time, into the main queue for another processing. Using transform methods, various performance measures are derived such as the joint distribution of the number of items in each queue and the dispatching rate. We then derive the optimal buffer size which minimizes the overall operating cost under a cost structure. This revised version was published online in June 2006 with corrections to the Cover Date.     

Modelling and analysis of Markovian continuous flow systems with a finite buffer

In this study, a Markovian fluid flow system with two stages separated by a finite buffer is considered. Fluid flow models have been analyzed extensively to evaluate the performance of production, computer, and telecommunication systems. Recently, we developed a methodology to analyze general Markovian continuous flow systems with a finite buffer. The flexibility of this methodology allows us to analyze a wide range of systems by specifying the transition rates and the flow rates associated with each state of each stage. In this study, in order to demonstrate the applicability of our methodology, we model and analyze a range of models studied in the literature. The examples we analyze as special cases of our general model include systems with phase-type failure and repair-time distributions, systems with machines that have multiple up and down states, and systems with multiple unreliable machines in series or parallel in each stage. For each case, the Markovian model is developed, the transition and flow rates are determined, and representative numerical results are obtained by using our methodology.     

A robust decomposition method for the analysis of production lines with unreliable machines and finite buffers

We consider production lines consisting of a series of machines separated by finite buffers. The processing time of each machine is deterministic and all the machines have the same processing time. All machines are subject to failures. As is usually the case for production systems we assume that the failures are operation dependent [3,7]. Moreover, we assume that the times to failure and the repair times are exponentially distributed. To analyze such systems, a decomposition method was proposed by Gershwin [13]. The computational efficiency of this method was later significantly improved by the introduction of the socalled DDX algorithm [5,6]. In general, this method provides fairly accurate results. There are, however, cases for which the accuracy of this decomposition method may not be acceptable. This is the case when the reliability parameters (average failure time and average repair time) of the different machines have different orders of magnitude. Such a situation may be encountered in real production lines. In [8], an improvement of Gershwin's original decomposition method has been proposed that in general provides more accurate results in the above mentioned situation. This other method is referred to as the Generalized Exponential (GE) method. The basic difference between the GEmethod and that of Gershwin is that it uses a twomoment approximation instead of a singlemoment approximation of the repair time distributions of the equivalent machines. There are, however, still cases for which the accuracy of the GEmethod is not as good as expected. This is the case, for example, when the buffer sizes are too small in comparison with the average repair time. We present in this paper a new decomposition method that is based on a better approximation of the repair time distributions. This method uses a threemoment approximation of the repair time distributions of the equivalent machines. Numerical results show that the new method is very robust in the sense that it seems to provide accurate results in all situations.     

Embedding linear spaces with two line degrees in finite projective planes

In this paper we shall classify all finite linear spaces with line degrees n and n-k having at most n²+n+1 lines. As a consequence of this classification it follows: If n is large compared with k, then any such linear space can be embedded in a projective plane of order n–1 or n.     

Asymptotic variance rate of the output in production lines with finite buffers

Production systems that can be modeled as discrete time Markov chains are considered. A statespacebased method is developed to determine the variance of the number of parts produced per unit time in the long run. This quantity is also referred to as the asymptotic variance rate. The block tridiagonal structure of the probability matrix of a general twostation production line with a finite buffer is exploited and a recursive method based on matrix geometric solution is used to determine the asymptotic variance rate of the output. This new method is computationally very efficient and yields a thousandfold improvement in the number of operations over the existing methods. Numerical experiments that examine the effects of system parameters on the variability of the performance of a production line are presented. The computational efficiency of the method is also investigated. Application of this method to longer lines is discussed and exact results for a threestation production line with finite interstation buffers are presented. A thorough review of the pertinent literature is also given.     

Consistency of perturbation analysis for a queue with finite buffer space and loss policy

The subject of discrete-event dynamical systems has taken on a new direction with the advent of perturbation analysis (PA), an efficient method for estimating the gradients of a steady-state performance measure, by analyzing data obtained from a single-simulation experiment in the time domain. A crucial issue is whether PA gives strongly consistent estimates, namely, whether average time-domain-based gradients converge, over infinite horizon, to the steady-state gradients. In this paper, we investigate this issue for a queue with a finite buffer capacity and a loss policy. The performance measure in question is the average amount of lost customers, as a function of the buffer's capacity, which is assumed to be continuous in our work. It is shown that PA gives strongly consistent estimates. The analysis uses a new technique, based on busy period-dependent inequalities. This technique may have possible extensions to analyses of consistency of PA for more general queueing systems.     

On a finite algebra with two operations

Acta Mathematica Hungarica -     

Time dependent analysis of finite buffer fluid flows and risk models with a dividend barrier

Based on the matrix-analytic approach to fluid flows initiated by Ramaswami, we develop an efficient time dependent analysis for a general Markov modulated fluid flow model with a finite buffer and an arbitrary initial fluid level at time 0. We also apply this to an insurance risk model with a dividend barrier and a general Markovian arrival process of claims with possible dependencies in successive inter-claim intervals and in claim sizes. We demonstrate the implementability and accuracy of our algorithms through a set of numerical examples that could also serve as test cases for comparing other solution approaches.      

Bounding the performance of tandem queues with finite buffer spaces

We consider a queueing system with three single servers in tandem with two intermediate buffer storages of finite capacity. The processing times are exponentially distributed and the first server has unlimited number of customers in front of it. Using a negative dependence property between the number of customers at the first and second buffer storages we show that a popular form of decomposition approach applied to this model, indeed, provides a lower bound for its performance. The approach used here to establish the bound is new and could be extended to establish bounds for other types of tandem queues with finite buffer spaces.     

Exact analysis of a two-workstation one-buffer flow line with parallel unreliable machines

This paper examines a model of a serial flow line with two workstations and an intermediate buffer. Each workstation consists of multiple unreliable parallel machines which are not necessarily identical, viz., the processing times, failure times and repair times of the parallel machines at each workstation are assumed to be exponentially distributed with non-identical mean rates. The system under consideration is solved via exact Markovian analysis. More specifically, a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity is developed and all possible transition equations are derived and solved analytically. Once the transition equations are solved the performance measures of the model under consideration can be easily evaluated. This model may be used as a decomposition block for solving larger flow lines with parallel unreliable machines at each workstation.     

On a synchronization queue with two finite buffers

In this paper, we consider a synchronization queue (or synchronization node) consisting of two buffers with finite capacities. One stream of tokens arriving at the system forms a Poisson process and the other forms a PH-renewal process. The tokens are held in the buffers until one is available from each flow, and then a group-token is instantaneously released as a synchronized departure. We show that the output stream of a synchronization queue is a Markov renewal process, and that the time between consecutive departures has a phase type distribution. Thus, we obtain the throughput of this synchronization queue and the loss probabilities of each type of tokens. Moreover, we consider an extended synchronization model with two Poisson streams where a departing group-token consists of several tokens in each buffer. This revised version was published online in June 2006 with corrections to the Cover Date.     

Optimal processing rate and buffer size of a jump-diffusion processing system

In this paper, we propose a reflected jump-diffusion model for processing systems with finite buffer size. We derive an analytic expression for the total expected discounted managing cost, which facilitates finding (numerically) the optimal processing rate and buffer size that minimize the total cost. Moreover, the formula for steady-state density of the processing system is obtained.