Determination of efficient simulation model fidelity for flexible manufacturing systems

Madan, Monish; Son, Young Jun; Cho, Hyunbo; Kulvatunyou, Boonserm

doi:10.1080/0951192052000288143

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…To verify the simulation models created, Madan et al (2005) analysed the results of a single part followed throughout the system. They state that model verification should be performed while the simulation model is in the development stage.…”

Section: Review Of Literaturementioning

confidence: 99%

Virtual modelling for simulation-based lean education

Cudney

Corns

Gadre

2014

IJLER

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This research presents a virtual simulation to enable students to explore and learn lean concepts through virtual experimentation. Exposing students to these concepts is crucial as demand for engineers with an in-depth understanding of lean philosophies is rising. The virtual simulation platform developed provides students an environment to apply lean concepts in experiments in the same manner as current methods for teaching lean, which include hands-on projects and simulation. The platform provides a simple interface, the ability to store results, and the modelling power of state of the art virtual reality software. The virtual simulation platform is built using the VE-Suite virtual engineering framework, which provides user-friendly dialogue boxes, graphical models, performance display gauges, and an editable layout. A mathematical model of an industrial process where lean can be implemented was created for this framework. In addition, a survey instrument is planned to gather information about strengths and weaknesses of the method to implement continuous improvement to the model. By working through this virtual simulation, students can experience multiple applications of lean in a real world setting without constructing an entire line or disrupting production in an existing line.

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Section: Review Of Literaturementioning

confidence: 99%

Virtual modelling for simulation-based lean education

Cudney

Corns

Gadre

2014

IJLER

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“…Generally, authors have developed different approaches: Generic frameworks like those of [20] or [21] provide rough guidance during the different stages of conceptual modeling, see [6] for similar frameworks. In a more specific approach [22] developed a framework for determining an efficient level of simulation detail based on simulation experiments and regression-based meta-models. Those frameworks aim to support the development of new models with an optimized LOC from scratch.…”

Section: Literature On Correct Level Of Complexitymentioning

confidence: 99%

The correct level of model complexity in semiconductor fab simulation — Lessons learned from practice

Rank

Hammel

Schmidt

et al. 2016

2016 27th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

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It is well-known that semiconductor manufacturing is one of the most complex types of production. Consequently, making the right decisions in order to get the optimum output of a semiconductor fab is quite challenging. For that reason, sophisticated discrete event simulation studies may be seen as a kind of magic tool for fab managers to face the ever-increasing complexity. But building complex dynamic simulation models in order to solve complex problems is not always the best solution. This article gives an overview of the discussion about the correct level of complexity of discrete event simulation models and lists the main reasons for the creation of overcomplex models. To provide guidance for practitioners, lessons learned are derived from four "real world" use cases in which the correct level of complexity was a major issue.

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“…Therefore, using sensitivity analysis techniques such as ANOVA to exclude insignificant ones from the set of decision variables and to aggregate several decision variables can help decision makers or optimization programs find the optimal or near optimal decisions more efficiently (Webster and Padgett 1984;Cassandras et al 2000;Rodriguez et al 2008). For the second issue, the number of simulation events should be reduced by conceptually aggregating multiple activities into one (Madan et al 2005;Huang et al 2009). This is often achieved by aggregating model building blocks into one or a few and objects into that of a larger size during the modeling stage.…”

mentioning

confidence: 98%

A SysML-based simulation model aggregation framework for seedling propagation system

Meng¹,

Kim²,

Son³

et al. 2013

2013 Winter Simulations Conference (WSC)

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This paper proposes a Systems Modeling Language (SysML)-based simulation model aggregation framework to develop aggregated simulation models with high accuracy. The framework consists of three major steps: 1) system conceptual modeling, 2) simulation modeling, and 3) additive regression model-based parameter estimation. SysML is first used to construct the system conceptual model for a generic seedling propagation system in terms of system structure and activities in a hierarchical manner (i.e. low, medium and high levels). Simulation models conforming to the conceptual model are then constructed in Arena. An additive regression model-based approach is proposed to estimate parameters for the aggregated simulation model. The proposed framework is demonstrated via one of the largest grafted seedling propagation systems in North America. The results reveal that 1) the proposed framework allows us to construct accurate but computationally affordable simulation models for seedling propagation system, and 2) model aggregation increases the randomness of simulation outputs. INTRODUCTIONGrafting is a horticultural technique whereby tissues from one plant are joined with another to obtain a combination of rootstock and scion genotypes that are more desirable than those contained in a single plant. The advantage of the vegetable grafting is to improve fruit yield and quality as well as to reduce the environmental impact through reducing chemical fumigants utilized in traditional cropping systems. According to Kubota et al. (2008), many seedling propagators in North America have strong interests in introducing grafting into their seedling propagation systems. For a seedling propagation system, uncertainties and complexity involved in high resource (e.g. machines and labors) management and in environmental factors (e.g. humidity and temperature) make the system performance difficult to predict. Discrete event simulation has become one of the most used analysis tools for large scale systems like seedling propagation system because it is one of the few tools that can take randomness into account, and it can address aggregate as well as very detailed models. A simulation model representing a seedling propagation system can be used to predict system performance, supporting various decisions in design or operation (e.g. production scheduling in a peak season) of the seedling propagation system. In simulation, fidelity refers to the faithfulness with which model behavior reflects modeled system behavior (Kim, Mcginnis and Zhou 2012). It has been recognized that the level of detail of simulation has a significant impact on the fidelity (Persson 2002;Vasudevan and Devikar 2011;Venkateswaran and Son 2004). For example, Venkateswaran and Son (2004) demonstrated that the dynamics and inventory level of supply chain are highly dependent on the level of detail of simulation models even though the models simulate the same process. One principal challenge that we faced while developing a highly detailed simulation model (i.e. low level sim...

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Determination of efficient simulation model fidelity for flexible manufacturing systems

Cited by 14 publications

References 25 publications

Virtual modelling for simulation-based lean education

Virtual modelling for simulation-based lean education

The correct level of model complexity in semiconductor fab simulation — Lessons learned from practice

A SysML-based simulation model aggregation framework for seedling propagation system

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