A simulation metamodelling based neural networks for lot-sizing problem in MTO sector

Hachicha, Wafik

doi:10.2507/ijsimm10(4)3.188

Cited by 27 publications

(14 citation statements)

References 13 publications

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“…Based on simulation outputs a multi-objective desirability optimization is achieved by using a Response surface methodology to minimize the number of runs. In another work [100], they developed a reverse simulation metamodels based ANN. The paradigm was tested in practical application, furthermore, ANN proved to be viable tool for stochastic simulation metamodeling.…”

Section: B Tactical Decision Planningmentioning

confidence: 99%

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

2020

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Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored.

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Section: B Tactical Decision Planningmentioning

confidence: 99%

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

2020

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“…Moreover, [14] develop a reverse simulation metamodel through artificial neural networks (ANNs). He obtain in optimal configuration that lot size of product P4, P5, P6, P7 and P8 would be set at 472 units, 286 units, 207 units, 675 units and 234 units, respectively.…”

Section: Validationmentioning

confidence: 99%

Combining Taguchi approach and response surface methodolgy for lot-sizing problem in MTO environnement

Ammeri¹,

Chabchoub²,

Hachicha

et al. 2015

2015 4th International Conference on Advanced Logistics and Transport (ICALT)

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The aim of this paper is to study a simulation optimization approach for the control of logistics flows.For this purpose, we evaluated the feasibility of integrating the Taguchi method and the response surface methodology (RSM). An academic case study which inspired from the literature is provided; the objective is to determine the optimal lot size for each manufactured product in the Sc.The comparative results with others metamodels methods indicate that this study presents a good performance of the Taguchi method combining with the RSM.

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“…It is difficult to obtain accurate temporal and spatial distribution characteristics of PM2.5 pollution; model simulation is the most efficient way to solve the problems of spatial analysis and prediction. PM2.5 concentration prediction methods include multivariate regression methods [ 7 , 8 , 9 ], genetic algorithms [ 10 , 11 ], grey [ 12 ] and Markov models [ 13 , 14 ], and artificial neural network models (ANN) [ 15 , 16 ]. An artificial neural network model is a network composed of a large number of neurons; compared with other forecasting models, the back propagation (BP) artificial neural network (BP-ANN) model is widely used to predict air pollution levels because of its high accuracy and ability to accurately map complex nonlinear problems [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

Zhang

Hong

He³

et al. 2015

IJERPH

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PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

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A simulation metamodelling based neural networks for lot-sizing problem in MTO sector

Cited by 27 publications

References 13 publications

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

Combining Taguchi approach and response surface methodolgy for lot-sizing problem in MTO environnement

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

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