A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

Li, Minqi

doi:10.1016/j.jmsy.2015.11.004

Cited by 43 publications

(9 citation statements)

References 85 publications

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“…Recently, a work based on metamodel and Monte Carlo simulation method have been done by Li et al (2016) applied in production planning of manufacturing system and compared with other approaches (e.g. mathematical programming).…”

Section: Continues Design Variables Direct Gradient Methodsmentioning

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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Section: Continues Design Variables Direct Gradient Methodsmentioning

confidence: 99%

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

Parnianifard¹,

Azfanizam²,

Ariffin³

et al. 2019

10.5267/j.dsl

View full text Add to dashboard Cite

show abstract

“…It is known that one of the ways to estimate an empirical function is by performing a regression analysis, which, according to Helene (2013) and Mendas and Lorenzoni (2018), has been widely used for such a purpose in several areas of expertise. Li et al (2016) developed a metamodel‐based MCS method to accurately capture the dynamic and the stochastic behaviour of a manufacturing system, and to allow real‐time evaluation of the performance metrics of a release plan.…”

Section: Mcdea Ovmcs and Empirical Functionsmentioning

confidence: 99%

“…The OLS (Ferraro & Giordani, 2012; Helene, 2013) was chosen among the diverse linear or non‐linear regression analyses. This is an algorithm that consists in developing a model through linear or non‐linear regression analyses and refining the parameters based on successive iterations (Li et al, 2016).…”

Section: Mcdea Ovmcs and Empirical Functionsmentioning

confidence: 99%

Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components

et al. 2021

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This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming ‐ Multiple Criteria Data Envelopment Analysis (WGP‐MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study.

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“…A well established and used set of practices are related to the operations research area, regarding discrete event simulation and optimisation methodologies applied on data treatment, capture patterns and scenarios evaluation to aid in the decision-making process [5,6]. In that sense, the metaheuristic class of optimisation techniques has been used to solve industrial problems in terms of production, logistics, related services and other issues [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Economic Lot-Size Using Machine Learning, Parallelism, Metaheuristic and Simulation

Sousa¹,

Montevechi²,

Miranda³

et al. 2019

Int. j. simul. model.

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The use of discrete event simulation optimisation methods is a tool commonly used as a decisionmaking support system in industrial problems, concerning management and resource allocation in order to maximise a set of values regarding costs, revenues and other enterprise interests. The present study has proposed and tested an optimisation algorithm developed on Python, with different wall clock time reduction strategies including parallelism, the Greedy Randomized Adaptive Search Procedure (GRASP) population-based metaheuristic, and ten machine learning methods. With the selected best machine learning method (Decision Trees Regressor) 6 optimisation scenarios were generated and then applied to an economic lot-size problem for a theoretical shop floor. The results showed improvements in the reduction of the processing time of 95.0 % comparing the serial GRASP with the parallel machine learning GRASP, obtaining a solution of 94.0 % of the best local optimum.

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A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

Cited by 43 publications

References 85 publications

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

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

Goal programming and multiple criteria data envelopment analysis combined with optimization and Monte Carlo simulation: An application in railway components

Economic Lot-Size Using Machine Learning, Parallelism, Metaheuristic and Simulation

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