Approximate analysis of serial flow lines with multiple parallel-machine stations

Diamantidis, Alexandros; Papadopoulos, Chrissoleon T.; Heavey, Cathal

doi:10.1080/07408170600838423

Cited by 37 publications

(14 citation statements)

References 26 publications

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“…A more extensive comparison of these algorithms is presented in Tsadiras, Papadopoulos, and O'Kelly (2012) leading to the same conclusions. Based on these results, the Myopic algorithm was chosen to be used for the experiments of this study in conjunction with the decomposition algorithm developed by Diamantidis, Papadopoulos, and Heavey (2006), since the large number of experiments makes the speed of the algorithm an important decision criterion. All the experiments were run on an available desktop PC with the following specifications: Windows XP Professional Version 2002 Service Pack 3, Intel Core 2 Duo CPU E8400@3.00 GHz, 2.00 GB RAM.…”

Section: Design Of the Production Line Experiments To Collect Traininmentioning

confidence: 99%

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

Tsadiras

Papadopoulos

O’Kelly

2013

Computers & Industrial Engineering

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Section: Design Of the Production Line Experiments To Collect Traininmentioning

confidence: 99%

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

Tsadiras

Papadopoulos

O’Kelly

2013

Computers & Industrial Engineering

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“…In order to improve production rate and/or reliability, a workstation has multiple machines in parallel [3]. Approximate solutions for the reliable lines with multiple machines using decomposition method are presented by [4,15,16]. The literature on the analysis of the unreliable systems with multiple machines in a workstation and finite buffer is more scarce than the reliable system.…”

Section: Yang Woo Shin and Dug Hee Moonmentioning

confidence: 99%

Throughput of flow lines with unreliable parallel-machine workstations and blocking

Shin¹,

Moon²

2017

Journal of Industrial &Amp; Management Optimization

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Flow lines in which workstations and buffers are linked along a single flow path one after another are widely used for modeling manufacturing systems. In this paper we consider the flow lines with multiple independent unreliable machines at each workstation and blocking. The processing times, time to failure and time to repair of each machine are assumed to exponentially distributed and blocking after service blocking protocol is also assumed. An approximate analysis for throughput in the flow lines is presented. The method developed here is based on the decomposition method using the subsystems with three workstations including virtual station and two buffers between workstations. Some numerical examples are presented for accuracy of approximation.

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“…The Buffers are defined by their size. If there is one upstream product, the layout of the line is always the same: Machine; Buffer; Machine; Buffer; Machine … It is assumed, that the first machine is never starved and the last machine is never blocked and machine failures are exponentially distributed [10,11]. A stochastic process is a random variable indexed by a parameter; we denote it by X(t).…”

Section: The Modelling Methodology For a Flow Linementioning

confidence: 99%

Production Systems Flow Modelling Using Decomposition Method and Required Buffers

Boteanu¹,

Zapciu²,

Cavalieri³

2017

Int. j. simul. model.

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This paper aims to provide an introduction to the analytical modelling and the discrete-events simulation for a manufacturing system. A general method of constructing a Markov chain and the simulation with discrete events using Delmia Quest software, are presented. In the present paper, those interested can find a real case study: modelling and simulation of the headrest support manufacturing line. The scientific contribution of this paper consists in the development of a theoretical model implemented in a real case study. The theoretical model estimates the production rate of the line and then, using this model we can establish how many parts in the buffers are required in order to achieve a production rate of X parts per minute. In the first phase, the manufacturing line is analysed, then the Markov chains for subsystems with two machines and a buffer are constructed. As the Markov chains need more time to provide solutions in queues theory, they can be used only for small production lines. To find the production rate for all the system, it is necessary to apply the Markov chains and the decomposition method. Those methods are implemented in the C/C++ programming. To validate the analytical model, the discrete-event simulation with Delmia Quest software is used. The article presents some comparisons between modelling with Markov chains and simulation with Delmia Quest software and provides a method of optimization of the buffers for the case study.

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Approximate analysis of serial flow lines with multiple parallel-machine stations

Cited by 37 publications

References 26 publications

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

Throughput of flow lines with unreliable parallel-machine workstations and blocking

Production Systems Flow Modelling Using Decomposition Method and Required Buffers

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