Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5798
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dc.contributor.advisorCheng, K-
dc.contributor.advisorOzbayrak, M-
dc.contributor.authorLancaster, John-
dc.date.accessioned2011-09-15T10:03:14Z-
dc.date.available2011-09-15T10:03:14Z-
dc.date.issued2007-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5798-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.en_US
dc.description.abstractThis thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class.en_US
dc.language.isoenen_US
dc.publisherBrunel University School of Engineering and Design PhD Theses-
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/5798/1/FulltextThesis.pdf-
dc.titleProject schedule optimisation utilising genetic algorithmsen_US
dc.typeThesisen_US
Appears in Collections:Mechanical and Aerospace Engineering
Dept of Mechanical Aerospace and Civil Engineering Theses

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