Production quantity estimation using an improved artificial neural network

By considering on the competitive market today, managing inventory becomes one factor that affected in improving business performance. This encouraged most industries to manage it efficiently by determining effective decision for inventory replenishment. For instance, mostly, industries decide next...

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Main Author:	Dzakiyullah, Raden Nur Rachman
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	QA Mathematics QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/15868/1/Raden%20Nur%20Rachman%20Dzakiyullah.pdf http://eprints.utem.edu.my/id/eprint/15868/2/Production%20quantity%20estimation%20using%20an%20improved%20artificial%20neural%20network.pdf
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id	my-utem-ep.15868
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Hussin, Burairah
topic	QA Mathematics QA76 Computer software
spellingShingle	QA Mathematics QA76 Computer software Dzakiyullah, Raden Nur Rachman Production quantity estimation using an improved artificial neural network
description	By considering on the competitive market today, managing inventory becomes one factor that affected in improving business performance. This encouraged most industries to manage it efficiently by determining effective decision for inventory replenishment. For instance, mostly, industries decide next inventory replenishment by considering on their last historical production. However, this decision cannot be implemented on the next production due to uncertainty/fluctuated condition. Therefore, poor decision on producing product will influence the business’ costs. Hence, this research proposes model based on Neural Network Back Propagation (NNBP) to estimate production quantity. This model is designed based on input variables that affect the determination of production quantity which include demand, setup costs, production, material costs, holding costs, transportation costs. The performance of NNBP can be analyzed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). In order to increase the performance of NNBP, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being hybrid with the ANN model to become Hybrid Neural Network Genetic Algorithm (HNNGA) model and Hybrid Neural Network Particle Swarm Optimization (HNNPSO) model respectively. These techniques were used to optimize attribute weighting on NNBP model. The proposed models were examined using private dataset that collected from Iron Casting Manufacturing in Klaten, Indonesia. Moreover, validation is conducted for all proposed models through both Cross-Validation and statistical analysis. The cross-validation is common technique used to prevent over fitting problem by dividing the data into two categories namely data training and data test. Meanwhile, statistical analysis considers normality test on error estimation and the significant difference among the proposed models. Experimental result shows that HNNGA and HNNPSO provide smaller measurement error that concurrently improves the performance of NNBP model. In this work, the proposed model contributes not only to update the original instrument, but also applicable and beneficial for industry, particularly in deciding effective inventory replenishment decision on production quantity.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Dzakiyullah, Raden Nur Rachman
author_facet	Dzakiyullah, Raden Nur Rachman
author_sort	Dzakiyullah, Raden Nur Rachman
title	Production quantity estimation using an improved artificial neural network
title_short	Production quantity estimation using an improved artificial neural network
title_full	Production quantity estimation using an improved artificial neural network
title_fullStr	Production quantity estimation using an improved artificial neural network
title_full_unstemmed	Production quantity estimation using an improved artificial neural network
title_sort	production quantity estimation using an improved artificial neural network
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2015
url	http://eprints.utem.edu.my/id/eprint/15868/1/Raden%20Nur%20Rachman%20Dzakiyullah.pdf http://eprints.utem.edu.my/id/eprint/15868/2/Production%20quantity%20estimation%20using%20an%20improved%20artificial%20neural%20network.pdf
_version_	1747833878559588352
spelling	my-utem-ep.158682022-05-17T16:08:46Z Production quantity estimation using an improved artificial neural network 2015 Dzakiyullah, Raden Nur Rachman QA Mathematics QA76 Computer software By considering on the competitive market today, managing inventory becomes one factor that affected in improving business performance. This encouraged most industries to manage it efficiently by determining effective decision for inventory replenishment. For instance, mostly, industries decide next inventory replenishment by considering on their last historical production. However, this decision cannot be implemented on the next production due to uncertainty/fluctuated condition. Therefore, poor decision on producing product will influence the business’ costs. Hence, this research proposes model based on Neural Network Back Propagation (NNBP) to estimate production quantity. This model is designed based on input variables that affect the determination of production quantity which include demand, setup costs, production, material costs, holding costs, transportation costs. The performance of NNBP can be analyzed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). In order to increase the performance of NNBP, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being hybrid with the ANN model to become Hybrid Neural Network Genetic Algorithm (HNNGA) model and Hybrid Neural Network Particle Swarm Optimization (HNNPSO) model respectively. These techniques were used to optimize attribute weighting on NNBP model. The proposed models were examined using private dataset that collected from Iron Casting Manufacturing in Klaten, Indonesia. Moreover, validation is conducted for all proposed models through both Cross-Validation and statistical analysis. The cross-validation is common technique used to prevent over fitting problem by dividing the data into two categories namely data training and data test. Meanwhile, statistical analysis considers normality test on error estimation and the significant difference among the proposed models. Experimental result shows that HNNGA and HNNPSO provide smaller measurement error that concurrently improves the performance of NNBP model. In this work, the proposed model contributes not only to update the original instrument, but also applicable and beneficial for industry, particularly in deciding effective inventory replenishment decision on production quantity. 2015 Thesis http://eprints.utem.edu.my/id/eprint/15868/ http://eprints.utem.edu.my/id/eprint/15868/1/Raden%20Nur%20Rachman%20Dzakiyullah.pdf text en public http://eprints.utem.edu.my/id/eprint/15868/2/Production%20quantity%20estimation%20using%20an%20improved%20artificial%20neural%20network.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=96083 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Hussin, Burairah 1. Adineh, V.R., Aghanajafi, C., Dehghan, G.H. and Jelvani, S., 2008. Optimization of the operational parameters in a fast axial flow CW CO2 laser using artificial neural networks and genetic algorithms. Optics & Laser Technology 40(8), pp. 1000–1007002E 2. 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