A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems

Li, Yan‐Fu; Ng, Szu Hui; Xie, Min; Goh, T.N.

doi:10.1016/j.asoc.2009.11.034

Cited by 160 publications

(100 citation statements)

References 37 publications

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“…Such values derive from both previous studies [9,50,51] and from some tests preliminarily performed by the authors in order to obtain a satisfactory compromise between the computational burden and the reliability of the results. …”

Section: Parameters Of the Genetic Algorithmmentioning

confidence: 99%

Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort

et al. 2015

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According to the increasing worldwide attention to energy and the environmental performance of the building sector, building energy demand should be minimized by considering all energy uses. In this regard, the development of building components characterized by proper values of thermal transmittance, thermal capacity, and radiative properties is a key strategy to reduce the annual energy need for the microclimatic control. However, the design of the thermal characteristics of the building envelope is an arduous task, especially in temperate climates where the energy demands for space heating and cooling are balanced. This study presents a novel methodology for optimizing the thermo-physical properties of the building envelope and its coatings, in terms of thermal resistance, capacity, and radiative characteristics of exposed surfaces. A multi-objective approach is adopted in order to optimize energy performance and thermal comfort. The optimization problem is solved by means of a Genetic Algorithm implemented in MATLAB ® , which is coupled with EnergyPlus for performing dynamic energy simulations. For demonstration, the methodology is applied to a residential building for two different Mediterranean climates: Naples and Istanbul. The results show that for Naples, because of the higher incidence of cooling demand, cool external coatings imply significant energy savings, whereas the OPEN ACCESS Sustainability 2015, 7 10810 insulation of walls should be high but not excessive (no more than 13-14 cm). The importance of high-reflective coating is clear also in colder Mediterranean climates, like Istanbul, although the optimal thicknesses of thermal insulation are higher (around 16-18 cm). In both climates, the thermal envelope should have a significant mass, obtainable by adopting dense and/or thick masonry layers. Globally, a careful design of the thermal envelope is always necessary in order to achieve high-efficiency buildings.

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Section: Parameters Of the Genetic Algorithmmentioning

confidence: 99%

Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort

et al. 2015

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“…A number of meta-modeling methods can be found in the literature (Jin et al 2001, Li et al 2010. Most popular among them are, response surface methods, artificial neural networks, multivariate adaptive regression splines, kriging, radial basis functions and support vector regression.…”

Section: Meta-modelsmentioning

confidence: 99%

On the scalability of meta-models in simulation-based optimization of production systems

Bandaru¹,

Ng²

2015

2015 Winter Simulation Conference (WSC)

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Optimization of production systems often involves numerous simulations of computationally expensive discrete-event models. When derivative-free optimization is sought, one usually resorts to evolutionary and other population-based meta-heuristics. These algorithms typically demand a large number of objective function evaluations, which in turn, drastically increases the computational cost of simulations. To counteract this, meta-models are used to replace expensive simulations with inexpensive approximations. Despite their widespread use, a thorough evaluation of meta-modeling methods has not been carried out yet to the authors' knowledge. In this paper, we analyze 10 different meta-models with respect to their accuracy and training time as a function of the number of training samples and the problem dimension. For our experiments, we choose a standard discrete-event model of an unpaced flow line with scalable number of machines and buffers. The best performing meta-model is then used with an evolutionary algorithm to perform multi-objective optimization of the production model.

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“…Other recent studies over SO methods can be found in two books Kleijnen, 2015) and four review papers (Barton, 1992;Carson & Maria, 1997;Li et al, 2010;Simpson, Poplinski et al, 2001). In general, SO models can be divided into two types of stochastic and deterministic models (Fig.…”

Section: Simulation-optimization (So)mentioning

confidence: 99%

“…Due to the stochastic nature of the simulation model, repeated runs of the simulation model with the same combination of input variables, lead to different outputs. Typically, as the training set to improve the metamodel, the average magnitude of repeated runs can be used (Li et al, 2010). If the stochastic simulation models is followed, each input combination , 1,2, … , is repeated times ( 1,2, .…”

Section: Robust Optimization In the Class Of Dual Responsementioning

confidence: 99%

Recent developments in metamodel based robust black-box simulation optimization: An overview

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2019

10.5267/j.dsl

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In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model's output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

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A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems

Cited by 160 publications

References 37 publications

Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort

Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort

On the scalability of meta-models in simulation-based optimization of production systems

Recent developments in metamodel based robust black-box simulation optimization: An overview

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