The Metamodel in Simulation Analysis: Can It Be Trusted?

Friedman, Linda Weiser; Pressman, Israel

doi:10.1057/jors.1988.160

Cited by 63 publications

(9 citation statements)

References 16 publications

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“…Meta models are taking a role as objective function in optimization process considering system boundaries [2,11,15]. Madu [22] presented major benefits of meta-modeling.…”

Section: Methodsmentioning

confidence: 99%

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

Dengız

İç

Belgin

2016

Mathematics and Computers in Simulation

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“…Meta models are taking a role as objective function in optimization process considering system boundaries [2,11,15]. Madu [22] presented major benefits of meta-modeling.…”

Section: Methodsmentioning

confidence: 99%

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

Dengız

İç

Belgin

2016

Mathematics and Computers in Simulation

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“…The parameter values of the chosen experimental factors are varied according to the method selected and the performance monitored (this usually means evaluating one or more KPI values or a function of KPIs) in order to locate particularly good (or bad) combinations (scenarios) and/or to improve understanding of how the factors influence performance (Robinson, 2004). Formal methods can be statistically based, such as experimental design (Box et al, 1978;Kleijnen et al, 2005), MM (Friedman and Pressman, 1988), or more mathematically or computationally based such as SO algorithms (Fu, 2002). Informal methods usually involve some kind of personal judgement on the part of the modeller and/or client and may resemble a more ad hoc version of one of the formal methods mentioned.…”

Section: Theory and Benefits Of Des Search Experimentation Techniquesmentioning

confidence: 99%

The use of search experimentation in discrete-event simulation practice

Hoad

Monks

O’Brien

2015

Journal of the Operational Research Society

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The practice of DES experimentation has not been rigorously assessed in over a decade. Past studies of DES practice report little transfer of experimentation theory into real-world application. We conducted an international survey of over 300 modellers to investigate the extent to which simulation optimisation, meta-modelling and design of experiments are used in practice. Over the last decade there has been substantial growth in the use of optimisation and to a lesser extent design of experiments to tackle practical problems. However, users rarely make use of optimisers bundled with commercial software, opting instead for custom or third-party solutions. Outside of academia, the use of methods is hampered by a lack of application knowledge and a persisting view that such techniques are not necessary. It is clear that academics must not become complacent regarding the dissemination of theory into common practice and continue to reach out to industry users.

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“…The advantages of metamodelling include model simpli®cation, enhanced exploration and interpretation of the model, generalization to other models of the same type, sensitivity analysis, optimization, answering to inverse questions and better understanding of the studied system and the interrelationships of design variables (Friedman and Pressman 1988).…”

Section: Post-metamodelling Analysismentioning

confidence: 99%

Design of manufacturing systems by a hybrid approach with neural network metamodelling and stochastic local search

Chen

Yang²

2002

International Journal of Production Research

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Metamodels are models of simulation models. Metamodels are able to estimate the simulation responses corresponding to a given combination of input variables. A simulation metamodel is easier to manage and provides more insights than simulation alone. Traditionally, the multiple regression analysis is utilized to develop the metamodel from a set of simulation experiments. Simulation can consequentially bene®t from the metamodelling in post-simulation analysis. A backpropagation (BP) neural network is a proven tool in providing excellent response predictions in many application areas and it outperforms regression analysis for a wide array of applications. In this paper, a BP neural network is used to generate metamodels for simulated manufacturing systems. For the purpose of optimal manufacturing systems design, mathematical models can be formulated by using the mapping functions generated from the neural network metamodels. The optimization model is then solved by a stochastic local search approach, simulated annealing (SA), to obtain an optimal con®guration with respect to the objective of the systems design. Instead of triggering the detailed simulation programs, the SA-based optimization procedure evaluates the simulation outputs by the neural network metamodels. By using the SA-based optimization algorithm, the solution space of the studied problem is extensively exploited to escape the entrapment of local optima while the number of time consuming simulation runs is reduced. The proposed methodology is illustrated to be both e ective and e cient in solving a manufacturing systems design problem through an example.

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The Metamodel in Simulation Analysis: Can It Be Trusted?

Cited by 63 publications

References 16 publications

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

The use of search experimentation in discrete-event simulation practice

Design of manufacturing systems by a hybrid approach with neural network metamodelling and stochastic local search

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