Learnable evolutionary model for flexible job-shop scheduling

196 p.

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Bibliographic Details
Main Author: Ho Nhu Binh
Other Authors: Tay Joc Cing
Format: Theses and Dissertations
Published: 2010
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Online Access:https://hdl.handle.net/10356/35728
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-357282023-03-04T00:46:42Z Learnable evolutionary model for flexible job-shop scheduling Ho Nhu Binh Tay Joc Cing School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Manufacturing::Production management 196 p. In recent years, resource allocation problems have received much attention in the research community and industry. The most frequently encountered problems of these topics are the Job-Shop Scheduling Problem (JSP). Effective scheduling for the JSP has the potential to decrease cost and increase profits. The JSP can be modeled as the allocation of machines over specific time to process a collection of jobs. An extension of the JSP which makes this problem more difficult is the Flexible Job-Shop Scheduling Problem (FJSP). Unfortunately, the traditional deterministic approaches, such as Branch and Bound, Priority Dispatching Rules, have scaled poorly with large problem size. The current local search techniques have also performed in expensive computational time. Currently, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using Evolutionary Algorithms (EAs). DOCTOR OF PHILOSOPHY (SCE) 2010-04-23T01:31:09Z 2010-04-23T01:31:09Z 2007 2007 Thesis Ho, N. B. (2007). Learnable evolutionary model for flexible job-shop scheduling. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/35728 10.32657/10356/35728 application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Manufacturing::Production management
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Manufacturing::Production management
Ho Nhu Binh
Learnable evolutionary model for flexible job-shop scheduling
description 196 p.
author2 Tay Joc Cing
author_facet Tay Joc Cing
Ho Nhu Binh
format Theses and Dissertations
author Ho Nhu Binh
author_sort Ho Nhu Binh
title Learnable evolutionary model for flexible job-shop scheduling
title_short Learnable evolutionary model for flexible job-shop scheduling
title_full Learnable evolutionary model for flexible job-shop scheduling
title_fullStr Learnable evolutionary model for flexible job-shop scheduling
title_full_unstemmed Learnable evolutionary model for flexible job-shop scheduling
title_sort learnable evolutionary model for flexible job-shop scheduling
publishDate 2010
url https://hdl.handle.net/10356/35728
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