Biologically inspired algorithims for job shop scheduling optimization

This project aims to explore and develop new biologically inspired algorithms for optimizing job shop scheduling problems. This project was motivated by the work carried out by Nakrani and Tovey (2004), on using a honey bee algorithm for dynamic allocation of Internet servers. In their algorithm, se...

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Main Author: Low, Malcolm Yoke Hean.
Other Authors: School of Computer Engineering
Format: Research Report
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/42342
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-423422023-03-03T20:22:01Z Biologically inspired algorithims for job shop scheduling optimization Low, Malcolm Yoke Hean. School of Computer Engineering DRNTU::Engineering::Manufacturing::Production management This project aims to explore and develop new biologically inspired algorithms for optimizing job shop scheduling problems. This project was motivated by the work carried out by Nakrani and Tovey (2004), on using a honey bee algorithm for dynamic allocation of Internet servers. In their algorithm, servers and HTTP request queues in an Internet server colony are modelled as foraging bees and flower patches respectively. In this project, we have successfully developed several bee colony optimization algorithms for the job shop scheduling, and have also extended the algorithms to other problem domains such as the travelling salesman problems as well as multi-objective simulation-based optimization for defence decision making process. The results of the finding from the project have been published in international journal and conferences. The work from the project has also led to the establishment of other research collaboration projects with external institutions in the domain of manufacturing, maritime as well as defence. SUG 10/07 2010-11-03T06:36:50Z 2010-11-03T06:36:50Z 2009 2009 Research Report http://hdl.handle.net/10356/42342 en 19 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Manufacturing::Production management
spellingShingle DRNTU::Engineering::Manufacturing::Production management
Low, Malcolm Yoke Hean.
Biologically inspired algorithims for job shop scheduling optimization
description This project aims to explore and develop new biologically inspired algorithms for optimizing job shop scheduling problems. This project was motivated by the work carried out by Nakrani and Tovey (2004), on using a honey bee algorithm for dynamic allocation of Internet servers. In their algorithm, servers and HTTP request queues in an Internet server colony are modelled as foraging bees and flower patches respectively. In this project, we have successfully developed several bee colony optimization algorithms for the job shop scheduling, and have also extended the algorithms to other problem domains such as the travelling salesman problems as well as multi-objective simulation-based optimization for defence decision making process. The results of the finding from the project have been published in international journal and conferences. The work from the project has also led to the establishment of other research collaboration projects with external institutions in the domain of manufacturing, maritime as well as defence.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Low, Malcolm Yoke Hean.
format Research Report
author Low, Malcolm Yoke Hean.
author_sort Low, Malcolm Yoke Hean.
title Biologically inspired algorithims for job shop scheduling optimization
title_short Biologically inspired algorithims for job shop scheduling optimization
title_full Biologically inspired algorithims for job shop scheduling optimization
title_fullStr Biologically inspired algorithims for job shop scheduling optimization
title_full_unstemmed Biologically inspired algorithims for job shop scheduling optimization
title_sort biologically inspired algorithims for job shop scheduling optimization
publishDate 2010
url http://hdl.handle.net/10356/42342
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