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dc.contributor.advisorHu, Yim Fun
dc.contributor.advisorLi, Jian-Ping
dc.contributor.authorBibiks, Kirils
dc.date.accessioned2019-11-14T07:43:57Z
dc.date.available2019-11-14T07:43:57Z
dc.identifier.urihttp://hdl.handle.net/10454/17439
dc.description.abstractThe main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. First, the developed algorithm is applied to a real-life software development project. Second, performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.en_US
dc.language.isoenen_US
dc.publisherUniversity of Bradforden_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectProject schedulingen_US
dc.subjectCuckoo searchen_US
dc.subjectSpecies conservationen_US
dc.subjectCombinatorial optimisationen_US
dc.subjectEvolutionary computationen_US
dc.titleScheduling and Resource Efficiency Balancing: Discrete Species Conserving Cuckoo Search for Scheduling in an Uncertain Execution Environmenten_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentFaculty of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2017
refterms.dateFOA2019-11-14T07:43:57Z


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