A comparison of forecasting models using multiple regression and artificial neural networks for the supply and demand of Thai ethanol

Homchalee, Rojanee; Sessomboon, Weerapat

doi:10.1109/ieem.2013.6962554

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Parameters of model were derived from our past research [2]. Moreover, they were forecasted by time series analysis and ANN [8]. This proposed model was solved by optimization modelling software, LINGO 11.0.…”

Section: Methodsmentioning

confidence: 99%

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Supply Chain Network Design of Ethanol in Thailand

Homchalee

Sessomboon

2015

AMM

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The proposed model is location-allocation model developed to design and manage the plants-to-customers ethanol supply chain in Thailand with the objective to minimize the total cost. The results showed that Thailand should have only one ethanol export depot and central depot located along wharfs in Samut Prakan province and along the highway in Non Sung district, Nakhon Ratchasima province, respectively. This model also provided the solutions on opening and expanding of production capacity of ethanol plants over time and appropriate ethanol allocation both of direct distribution and through the central depot for long term (2012-2021).

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Section: Methodsmentioning

confidence: 99%

“…Equation ( 5) ensures that amount of ethanol have sufficient production for distribution. Moreover, the amount of ethanol shipped to customer must meet both domestic and export demands, as indicated by ( 6)- (8). Equation ( 9) indicates the balance of ethanol shipped to and from central depots.…”

Section: Mathematical Modelmentioning

confidence: 99%

Supply Chain Network Design of Ethanol in Thailand

Homchalee

Sessomboon

2015

AMM

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Prediction of tool wear length in micro-EDM process using an artificial neural network

Kanake

Ahuja²

2022

Materials Today: Proceedings

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An Approach to Obsolescence Forecasting based on Hidden Markov Model and Compound Poisson Process

Grichi

Beauregard

Dao

2019

IJIEOM

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The popularity of electronic devices has sparked research to implement components that can achieve better performance and scalability. However, companies face significant challenges when they use systems with a long-life cycle, such as in avionics, which leads to obsolescence problems. Obsolescence can be driven by many factors, primary among which could be the rapid development of technologies that lead to a short life cycle of parts. Moreover, obsolescence problems can prove costly in terms of intermittent stock availability and unmet demand. Therefore, obsolescence forecasting appears to be one of the most efficient solutions. This paper presents a review of gaps in the actual approaches and proposes a method that can better forecast the product life cycle. The proposed approach will help companies to improve obsolescence forecasting and reduce its impact in the supply chain. The method introduces a stochastic approach to estimate the obsolescence life cycle through simulation of demand data using Markov chain and homogeneous compound Poisson process. This approach uses multiple states of the life cycle curve based on the change in demand rate and introduces hidden Markov theory to estimate the model parameters. Numerical results are provided to validate the proposed method. To examine the accuracy of this approach, the standard deviation (STD) of obsolescence time is calculated. The results showed that the life cycle curves of parts can be predicted with high accuracy.

show abstract

A comparison of forecasting models using multiple regression and artificial neural networks for the supply and demand of Thai ethanol

Cited by 3 publications

References 8 publications

Supply Chain Network Design of Ethanol in Thailand

Supply Chain Network Design of Ethanol in Thailand

Prediction of tool wear length in micro-EDM process using an artificial neural network

An Approach to Obsolescence Forecasting based on Hidden Markov Model and Compound Poisson Process

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