Cover Image

Hybrid optimization approach to estimate random demand

The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in...

Full description

Saved in:
Bibliographic Details
Main Authors: Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman
Format: Conference or Workshop Item
Language:English
Published: 2012
Subjects:
QA76 Computer software
Online Access:http://repo.uum.edu.my/6962/1/P2_-_ICCIS.pdf
http://repo.uum.edu.my/6962/
http://dx.doi.org/10.1109/ICCISci.2012.6297292
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uum.repo.6962
record_format eprints
spelling my.uum.repo.69622013-01-21T01:28:19Z http://repo.uum.edu.my/6962/ Hybrid optimization approach to estimate random demand Wahab, Musa Ku-Mahamud, Ku Ruhana Yasin, Azman QA76 Computer software The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. One was the fitness evaluation in the demand forecasting model in which more than one variable was included, and the other was accuracy of the demand forecasting model to predict the future projection of random energy demand. A local search was proposed to assist in overcoming the first problem.It was used to approximate the input-output response relationship underlying random energy demand forecasting models which was then incorporated into the hybrid algorithm to reduce the local optima problem.To assist in overcoming the second problem, scenario analyses were adopted to determine the future projection of random energy demand. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/6962/1/P2_-_ICCIS.pdf Wahab, Musa and Ku-Mahamud, Ku Ruhana and Yasin, Azman (2012) Hybrid optimization approach to estimate random demand. In: International Conference on Computer & Information Science (ICCIS), 12-14 June 2012, Kuala Lumpur, Malaysia. http://dx.doi.org/10.1109/ICCISci.2012.6297292 doi:10.1109/ICCISci.2012.6297292
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Wahab, Musa
Ku-Mahamud, Ku Ruhana
Yasin, Azman
Hybrid optimization approach to estimate random demand
description The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. One was the fitness evaluation in the demand forecasting model in which more than one variable was included, and the other was accuracy of the demand forecasting model to predict the future projection of random energy demand. A local search was proposed to assist in overcoming the first problem.It was used to approximate the input-output response relationship underlying random energy demand forecasting models which was then incorporated into the hybrid algorithm to reduce the local optima problem.To assist in overcoming the second problem, scenario analyses were adopted to determine the future projection of random energy demand.
format Conference or Workshop Item
author Wahab, Musa
Ku-Mahamud, Ku Ruhana
Yasin, Azman
author_facet Wahab, Musa
Ku-Mahamud, Ku Ruhana
Yasin, Azman
author_sort Wahab, Musa
title Hybrid optimization approach to estimate random demand
title_short Hybrid optimization approach to estimate random demand
title_full Hybrid optimization approach to estimate random demand
title_fullStr Hybrid optimization approach to estimate random demand
title_full_unstemmed Hybrid optimization approach to estimate random demand
title_sort hybrid optimization approach to estimate random demand
publishDate 2012
url http://repo.uum.edu.my/6962/1/P2_-_ICCIS.pdf
http://repo.uum.edu.my/6962/
http://dx.doi.org/10.1109/ICCISci.2012.6297292
_version_ 1644279409664851968
score 13.211869

Similar Items