Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/27250
Title: Improvement of Performance measures In multi level assembly job shops Using particle swarm optimization And genetic algorithm
Researcher: Krishnamurthy, K
Guide(s): Mohanasundaram, K M
Keywords: Genetic Algorithm
Non deterministic Polynomial
Particle Swarm Optimization
Upload Date: 29-Oct-2014
University: Anna University
Completed Date: 01/12/2009
Abstract: Assembly job shop scheduling problems that optimize lead newlineTime and tardiness are usually much more computationally complex and newlineare classified as strongly Non deterministic Polynomial NP hard type newlineParticle Swarm Optimization PSO Algorithm and Genetic Algorithm newline GA are able to find near optimal solutions for a wide range of newlinecombinatorial optimization problems Earlier research studies in newline scheduling of operations in job shops processing multi level assembly newlinejobs have revealed that no single rule was able to perform well for all newline measures of performance Panwalkar and Iskander 1977 Haupt 1989 newline The dispatching rules that performed well in simple job shops are not necessarily appropriate for assembly job shops Ramasesh 1990 newline
Pagination: xxi, 124p.
URI: http://hdl.handle.net/10603/27250
Appears in Departments:Faculty of Mechanical Engineering

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04_acknowledgement.pdf6.64 kBAdobe PDFView/Open
05_content.pdf27.92 kBAdobe PDFView/Open
06_chapter1.pdf10.23 kBAdobe PDFView/Open
07_chapter2.pdf20.64 kBAdobe PDFView/Open
08_chapter3.pdf55.67 kBAdobe PDFView/Open
09_chapter4.pdf25.86 kBAdobe PDFView/Open
10_chapter5.pdf175.15 kBAdobe PDFView/Open
11_chapter6.pdf36.79 kBAdobe PDFView/Open
12_chapter7.pdf104.69 kBAdobe PDFView/Open
13_chapter8.pdf10.66 kBAdobe PDFView/Open
14_appendix.pdf292.12 kBAdobe PDFView/Open
15_reference.pdf18.45 kBAdobe PDFView/Open
16_publication.pdf7.36 kBAdobe PDFView/Open
17_vitae.pdf8.74 kBAdobe PDFView/Open
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