&amp;#x201C;Pull&amp;#x201D; replenishment performance as a function of demand rates and setup times under optimal settings

Enns,

doi:10.1109/wsc.2007.4419782

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…In other words, the larger the S metric value, the higher the TP/CT ratio, indicating that higher TP can be achieved with the same CT. With the example in Figure 12, the fact that PCM A outperforms PCM B (see also Figure 1) can now be quantitatively verified by showing S A > S B . In contrast to the technique that calculates the Trade-off Curves Areas (denoted as ∆ ) by assuming straight line segments between the points along the trade-off curves (Enns 2007) (Enns and Roger 2008), hypervolume can be readily applied to deal with problems that have more than two objectives. At the same time, without knowing the existence of possible points between two consecutive solutions lying next to each other on the Pareto front, it can be argued that assuming a straight line would increase the estimation error (Knowles 2008).…”

Section: Results and Analysismentioning

confidence: 99%

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A comparative study of production control mechanisms using simulation-based multi-objective optimisation

Bernedixen

Syberfeldt

2012

International Journal of Production Research

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There exist many studies conducted to compare the performance of different production control mechanisms (PCMs) in order to determine which one performs the best under different situations. Nonetheless, most of these studies suffer from the problems that the PCMs are not compared with their optimal parameter settings in a truly multi-objective context. This paper describes how different PCMs can be compared under their optimal settings through generating the Pareto-optimal frontiers, in form of optimal trade-off curves in the performance space, by applying evolutionary multi-objective optimization to simulation models. This concept is illustrated with a bi-objective comparative study of the four most popular PCMs in the literature, namely Push, Kanban, CONWIP and DBR, on an unbalanced serial flow line in which both control parameters and buffer capacities are to be optimized. Additionally, it introduces the use of normalized hypervolume as the quantitative metric and confidence-based significant dominance as the statistical analysis method to verify the differences of the PCMs in the performance space. While the results from this unbalanced flow line cannot be generalized, it indicates clearly that a PCM may be preferable in certain regions of the performance space, but not others, which supports the argument that PCM comparative studies have to be performed within a Paretobased multi-objective context.

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Section: Results and Analysismentioning

confidence: 99%

“…More recent studies that propose the generation of best trade-off curves to compare the performance of PCMs via simulation can be found in Enns (2007), Rogers (2008), andMacDonald andGunn, (2008).…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative study of production control mechanisms using simulation-based multi-objective optimisation

Bernedixen

Syberfeldt

2012

International Journal of Production Research

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“…Many studies have been conducted in order to define the optimal parameters of these control mechanisms [9][10][11][12][13], but also there are several studies answering the question of which control mechanism is a better choice in a given production setting. [14][15][16][17][18][19][20][21][22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Pull Production Control Mechanisms by Simulation

Tošanović

Štefanić

2021

Processes

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Today, companies need to continuously improve their production processes, which is a complex task. Lean manufacturing is one of the methodologies for production improvement, and one of the basic goals of any lean implementation is to reduce work-in-process (WIP) and shorten the production lead time. One of the basic lean principles for achieving these goals is pull principle. The adoption of this principle is quite challenging, as it requires a long-term commitment in the application and adoption of various lean techniques and tools that are prerequisites for the successful introduction of the pull principle. Kanban is the most well-known pull production control mechanism, and the first one developed within Toyota production system, but later, other pull control mechanisms were developed. Some of them include Conwip, Hybrid Kanban/Conwip, and Drum Buffer Rope (DBR), and those three, together with Kanban, were the research topic of this study. These four mechanisms were not explored and compared all together not for these specific production configurations considered in this research but also with regard to optimal parameters of control mechanisms. The goal was to analyze and compare how these pull control mechanisms affect lead time in different production conditions. For this purpose, simulation experiments were performed. The results showed that for different production conditions, different pull control mechanisms are optimal in terms of shortening lead time. This finding could help companies as a guideline for making a decision in terms of which pull control mechanism to choose.

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“…Enns analyzed a model for total inventory and delivery performance by mathematical formation, and compared with simulation results by using experimental design methods (Enns, 2007). One of current major application is semiconductor manufacturing system.…”

Section: Simulation Technologies Appeared In Winter Simulation Confermentioning

confidence: 99%

A Simulation Technology for Supply-Chain Ingeration

Umeda¹

2010

Discrete Event Simulations

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“Pull” replenishment performance as a function of demand rates and setup times under optimal settings

Cited by 6 publications

References 6 publications

A comparative study of production control mechanisms using simulation-based multi-objective optimisation

A comparative study of production control mechanisms using simulation-based multi-objective optimisation

Evaluation of Pull Production Control Mechanisms by Simulation

A Simulation Technology for Supply-Chain Ingeration

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