Artificial neural network models for lot-sizing problem: a case study

Şenyiğit, Ercan; Atıcı, Uğur

doi:10.1007/s00521-012-0863-z

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…ANN was most widely used mathematical algorithms for overcoming nonlinearity as the presence of nonlinearity in the spectra, changes of the effective light path, experimental conditions, and instrument variations. [ 8 17 18 19 20 21 ]…”

Section: Methodsmentioning

confidence: 99%

Rapid determination of puerarin by near-infrared spectroscopy during percolation and concentration process of puerariae lobatae radix

et al. 2016

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Background:Gegen (Puerariae Labatae Radix) is one of the important medicines in Traditional Chinese Medicine. The studies showed that Gegen and its preparation had effective actions for atherosclerosis.Objective:Near-infrared (NIR) was used to develop a method for rapid determination of puerarin during percolation and concentration process of Gegen.Materials and Methods:About ten batches of samples were collected with high-performance liquid chromatography analysis values as reference, calibration models are generated by partial least-squares (PLS) regression as linear regression, and artificial neural networks (ANN) as nonlinear regression.Results:The root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r2) was 97.79% and 98.47%, respectively.Conclusions:The NIR model for the rapid analysis of puerarin can be used for on-line quality control in the percolation and concentration process.SUMMARY Near-infrared was used to develop a method for on-line quality control in the percolation and concentration process of GegenCalibration models are generated by partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regressionThe root mean square error of prediction for the PLS and ANN model was 0.0396 and 0.0365 and correlation coefficients (r2) was 97.79% and 98.47%, respectively. Abbreviations used: NIR: Near-Infrared Spectroscopy; Gegen: Puerariae Loabatae Radix; TCM: Traditional Chinese Medicine; PLS: Partial least-squares; ANN: Artificial neural networks; RMSEP: Root mean square error of validation; R2: Correlation coefficients; PAT: Process analytical technology; FDA: The Food and Drug Administration; Rcal: Calibration set; RMSECV: Root mean square errors of cross-validation; RPD: Residual predictive deviation; SLS: Straight Line Subtraction; MLP: Multi-Layer Perceptron; MSE: Mean square error.

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Section: Methodsmentioning

confidence: 99%

Rapid determination of puerarin by near-infrared spectroscopy during percolation and concentration process of puerariae lobatae radix

et al. 2016

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“…In the work of Senyiğit and Atici (2013), 40 different configurations of an ANN are studied for solving a dynamic single-product lotsizing problem without capacity constraints. Eight different activation functions are combined with five different learning rules.…”

Section: Ann In Lotsizingmentioning

confidence: 99%

A machine learning approach for predicting the best solution heuristic for a large scaled Capacitated Lotsizing Problem

Kärcher,

Meyr

2023

Preprint

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For some NP-hard lotsizing problems, many different solution heuristics exist, but they have different solution qualities and computation times depending on the characteristics of the problem instance. The computation times of the individual solution heuristics increase significantly with the problem size, so that testing all available solution heuristics for large problem instances requires extensive time. Therefore, it is necessary to develop a method that allows a prediction of the best solution heuristic for the respective problem instance without testing all available solution heuristics. The Capacitated Lotsizing Problem (CLSP) is chosen as the problem to be solved, since it is well researched and several different solution heuristics exist for it. The CLSP addresses the problem of determining lotsizes on a production line given limited capacity, product-dependent setup costs, and deterministic, dynamic demand for multiple products. The objective is to minimize setup and inventory holding costs. Five different forecasting methods are presented. One of them is a two-layer neural network called CLSP-Net. It is trained on small problem instances, which can be solved very fast with the considered solution heuristics. Nevertheless, CLSP-Net is able to predict the best solution heuristic even for large problem instances.

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“…Multilayer perceptron (MLP) is a type of neural network [51]. MLP is a supervised learning algorithm that includes three layers, namely the input layer, hidden layer, and output.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Gürcan¹,

Beyca²,

Doğan³

2021

IJCIS

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Diabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/healthtopics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lowerdimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes.

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Artificial neural network models for lot-sizing problem: a case study

Cited by 12 publications

References 15 publications

Rapid determination of puerarin by near-infrared spectroscopy during percolation and concentration process of puerariae lobatae radix

Rapid determination of puerarin by near-infrared spectroscopy during percolation and concentration process of puerariae lobatae radix

A machine learning approach for predicting the best solution heuristic for a large scaled Capacitated Lotsizing Problem

A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

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