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A decision support system for improving forecast using genetic algorithm and tabu search

The intrinsic uncertainties associated with demand forecasting become more acute when it is required to provide invaluable dimensions for the decision-making process. The concept of decision support system (DSS) is very broad and it can take many different forms. In general, we can say that a DSS is...

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Main Author: Ismail, Zuhaimy
Format: Article
Language:English
Published: Asian Research Publishing Network (ARPN) 2008
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/9947/1/ZuhaimyIsmail2008_ADecisionSupportSystemForImprovingForecast.pdf
http://eprints.utm.my/id/eprint/9947/
http://www.arpnjournals.com/jeas/research_papers/rp_2008/jeas_0608_100.pdf
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spelling my.utm.99472011-05-10T04:58:57Z http://eprints.utm.my/id/eprint/9947/ A decision support system for improving forecast using genetic algorithm and tabu search Ismail, Zuhaimy Q Science (General) The intrinsic uncertainties associated with demand forecasting become more acute when it is required to provide invaluable dimensions for the decision-making process. The concept of decision support system (DSS) is very broad and it can take many different forms. In general, we can say that a DSS is a computerized system for assisting decision making. Forecasting models has been recognized as one of the tools used in DSS. The need and relevance of forecasting tools has become a much-discussed issue and this has led to the development of various new tools and methods for forecasting in the last two decades. One traditional tool for forecasting time series data is the Winter’s method with three parameters that determine the accuracy of the model. The search for the best parameter value of ?, ? and ? and their combinations using trial and error method is time consuming. Hence, a good optimization technique is required to select the best parameter value to minimize the fitness function. We employ the unique search of Genetic Algorithm (GA) to generate and search for the best value and due to the nature of GA that is based on random search; the near optimum solution could be improved by the introduction of a more systematic search known as Tabu Search (TS). Our study shows that combining both GA and TS search methods generate a more accurate forecast Asian Research Publishing Network (ARPN) 2008-06 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/9947/1/ZuhaimyIsmail2008_ADecisionSupportSystemForImprovingForecast.pdf Ismail, Zuhaimy (2008) A decision support system for improving forecast using genetic algorithm and tabu search. ARPN Journal of Engineering and Applied Sciences, 3 (3). pp. 13-16. ISSN 1819-6608 http://www.arpnjournals.com/jeas/research_papers/rp_2008/jeas_0608_100.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Ismail, Zuhaimy
A decision support system for improving forecast using genetic algorithm and tabu search
description The intrinsic uncertainties associated with demand forecasting become more acute when it is required to provide invaluable dimensions for the decision-making process. The concept of decision support system (DSS) is very broad and it can take many different forms. In general, we can say that a DSS is a computerized system for assisting decision making. Forecasting models has been recognized as one of the tools used in DSS. The need and relevance of forecasting tools has become a much-discussed issue and this has led to the development of various new tools and methods for forecasting in the last two decades. One traditional tool for forecasting time series data is the Winter’s method with three parameters that determine the accuracy of the model. The search for the best parameter value of ?, ? and ? and their combinations using trial and error method is time consuming. Hence, a good optimization technique is required to select the best parameter value to minimize the fitness function. We employ the unique search of Genetic Algorithm (GA) to generate and search for the best value and due to the nature of GA that is based on random search; the near optimum solution could be improved by the introduction of a more systematic search known as Tabu Search (TS). Our study shows that combining both GA and TS search methods generate a more accurate forecast
format Article
author Ismail, Zuhaimy
author_facet Ismail, Zuhaimy
author_sort Ismail, Zuhaimy
title A decision support system for improving forecast using genetic algorithm and tabu search
title_short A decision support system for improving forecast using genetic algorithm and tabu search
title_full A decision support system for improving forecast using genetic algorithm and tabu search
title_fullStr A decision support system for improving forecast using genetic algorithm and tabu search
title_full_unstemmed A decision support system for improving forecast using genetic algorithm and tabu search
title_sort decision support system for improving forecast using genetic algorithm and tabu search
publisher Asian Research Publishing Network (ARPN)
publishDate 2008
url http://eprints.utm.my/id/eprint/9947/1/ZuhaimyIsmail2008_ADecisionSupportSystemForImprovingForecast.pdf
http://eprints.utm.my/id/eprint/9947/
http://www.arpnjournals.com/jeas/research_papers/rp_2008/jeas_0608_100.pdf
_version_ 1643645291310612480
score 13.211869

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