Analysing and forecasting the security in supply-demand management of Chinese forestry enterprises by linear weighted method and artificial neural network

Zhao, Xuan; Yue, Shangzhi

doi:10.1080/17517575.2020.1739343

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…It was observed that the ANN and the ARIMA models were far more accurate as compared to the baseline model which was used at Zahedan Blood Transfusion Center to calculate demand volatility Jahangir et al [ 34 ] In this study, an AI-based forward feed and recurrent ANN with an LM training module have been developed to predict the PEV-TB. With the help of the designed model, the net economic loss was reduced by approximately $16 per year per PEV when compared with conventional methods Jebaraj et al [ 62 ] This study showed that the ANN-based model can be utilized for forecasting coal demand in India as this model gave less MPE and higher R 2 scores for most cases Zhao and Yue [ 63 ] The multi-objective linear weighted approach and ANN model are utilized to forecast the security in the supply management of the forest industry Loureiro et al [ 64 ] This study looks into how a DL model can be used in the estimation of sales in the fashion industry. The sales prediction obtained via the DL technique has also been compared with shallow techniques such as decision trees, RF, and SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

et al. 2022

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Demand forecasting has been a major concern of operational strategy to manage the inventory and optimize the customer satisfaction level. The researchers have proposed many conventional and advanced forecasting techniques, but no one leads to complete accuracy. Forecasting is equally important in manufacturing as well as retail companies. In this study, the performances of five regression techniques of machine learning, viz. random forest (RF), extreme gradient boosting (XGBoost), gradient boosting, adaptive boosting (AdaBoost), and artificial neural network (ANN) algorithms, are compared with a proposed hybrid (RF-XGBoost-LR) model for sales forecasting of a retail chain considering the various parameters of forecasting accuracy. The weekly sales data of a US-based retail company is considered in the analysis of the forecasts undertaking the attributes affecting the sale such as the temperature of the region and the size of the store. It is observed that the hybrid RF-XGBoost-LR outperformed the other models measured against various metrics of performance. This study may help the industry decision-maker to understand and improve the methods of forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

et al. 2022

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“…The experimental results illustrated the proposed system can decrease process time, cut down processing cost, and make better accuracy. Zhao and Yue (2020) developed an early-warning system based on a linear weighted method and artificial neural network to analyse and forecast the security for Chinese forestry enterprises. Ko (2020) developed a hybrid intelligent empty container dispatching system model based on fuzzy set theory and genetic algorithm.…”

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%

Cloud & fog computing: intelligent applications

Chen

Lughofer

Eğrioğlu

2021

Enterprise Information Systems

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RETRACTED CHAPTER: Neural Network Configuration for Pollen Analysis

Viloria

Mercado

Lezama

2020

Advances in Intelligent Systems and Computing

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Palynology is a botanical discipline devoted to the study of pollen and spores [1], focusing mainly on the analysis of the external morphology that presents structural patterns different from those of the variations in the exine, which is the external wall of the pollen grains. The study and microscopic analysis of its symmetry, wall opening, contour, shape, size, etc., have a taxonomic value and allows distinguishing different taxa at different levels: family, genera, species. The study of pollen grains is a difficult task, in its different phases, from small microscopic samples. The analysis of these is an important source of information for many scientific and industrial applications, making palynology a valuable tool for various areas of knowledge [1]. In palynology, neural networks have been successfully applied for the classification of pollen grains. For this purpose, RPROP was selected as a neural network training algorithm for the classification of a previously reported dataset.

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Analysing and forecasting the security in supply-demand management of Chinese forestry enterprises by linear weighted method and artificial neural network

Cited by 7 publications

References 23 publications

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach

Cloud & fog computing: intelligent applications

RETRACTED CHAPTER: Neural Network Configuration for Pollen Analysis

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