Efficient methods and techniques for the open-shop scheduling problem. (c2006)

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dc.contributor.author Bou Ghosn, Steve
dc.date.accessioned 2011-11-14T09:03:51Z
dc.date.available 2011-11-14T09:03:51Z
dc.date.copyright 2006 en_US
dc.date.issued 2011-11-14
dc.date.submitted 2006-11-29
dc.identifier.uri http://hdl.handle.net/10725/978
dc.description Bibliography: leaves 82-83. en_US
dc.description.abstract In this paper we investigate the use of two different heuristic techniques to the openshop scheduling problem and we make a comparison between them. The open-shop scheduling problem is NP hard and due to that it's very important to find heuristic approaches that can generate better approximate solutions. This work first focuses on solving the open-shop scheduling problem using genetic algorithms. We present an interesting implementation of genetic operators that combines the use of deterministic moves and pure random moves. We then perform tuning and testing of our approach and present detailed results for each problem instance of the Taillard benchmarks. We also compare our results with those obtained in other recent research works on the subject. In the second part of our work we focus on an approach based on simulated annealing. We perform tuning and testing for our annealing approach and present detailed result comparisons for all the Taillard Benchmarks. Finally we compare both the results obtained by ga and annealing and conclude that even though all results are good, in general our annealing implementation seems to perform better than our GA implementation, especially for larger problem sizes. We also justify the reasons why we think our ga approach didn't perform as good as the annealing. en_US
dc.language.iso en en_US
dc.subject Production scheduling en_US
dc.subject Computer algorithms en_US
dc.title Efficient methods and techniques for the open-shop scheduling problem. (c2006) en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Science en_US
dc.author.school Arts and Sciences en_US
dc.author.idnumber 199805410 en_US
dc.author.commembers Dr. Danielle Azar
dc.author.commembers Dr. Chadi Nour
dc.author.woa OA en_US
dc.description.physdesc 1 bound copy: v, 83 leaves; ill., tables; 30 cm. available at RNL. en_US
dc.author.division Computer Science en_US
dc.author.advisor Dr. Haidar Harmanani
dc.identifier.doi https://doi.org/10.26756/th.2006.58 en_US
dc.publisher.institution Lebanese American University en_US

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