Performance evaluation of Metaheuristics Search Techniques in resource allocation problems
| aut.embargo | No | en_NZ |
| aut.thirdpc.contains | No | en_NZ |
| aut.thirdpc.permission | No | en_NZ |
| aut.thirdpc.removed | No | en_NZ |
| dc.contributor.advisor | Andrew, Connor | |
| dc.contributor.author | Shah, Amit Anilkumar | |
| dc.date.accessioned | 2012-06-18T23:31:53Z | |
| dc.date.available | 2012-06-18T23:31:53Z | |
| dc.date.copyright | 2011 | |
| dc.date.created | 2011 | |
| dc.date.issued | 2011 | |
| dc.date.updated | 2012-06-18T19:40:53Z | |
| dc.description.abstract | Existing research has focused on solving problems in the area of project management using variety of approaches including search based software engineering approach. The main aim of this research is to evaluate the performance of metaheuristics search techniques such as genetic algorithm, simulated annealing and tabu search in resource allocation problem with project management discipline. This will enable the paper to introduce an alternative approach to solve this Resource Constrained Project Scheduling Problem (RCPSP). The nature of this research is both constructive and experimental therefore software development research methodology will be utilised as a guideline. This study reports a comprehensive set of experiments which evaluate the performance of metaheuristics search techniques. Initial set of experiments were performed over various numerical test function to verify the implementation of search techniques. The next stage of experiments had focused on the scalability of these search techniques. Based on the first two experiments, search techniques were evaluated against a discrete problem to further explore the scalability. Finally, a multi objective test case problem was evaluated which focused around RCPSP. For each of these experiments the parameters were fine-tuned during the design phase of the experiments. Based on the experiments, it was apparent that the metaheuristics search techniques can be used to solve problems in resource allocation within project management discipline. Finally, a comparison analysis strongly suggests that overall simulated annealing had performed better than genetic algorithm and tabu search. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/4463 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Software engineering | en_NZ |
| dc.subject | Search based software engineering | en_NZ |
| dc.subject | Metaheuristics search techniques | en_NZ |
| dc.subject | Resource constrained project scheduling problem | en_NZ |
| dc.subject | Cost allocation | en_NZ |
| dc.subject | Project management | en_NZ |
| dc.title | Performance evaluation of Metaheuristics Search Techniques in resource allocation problems | en_NZ |
| dc.type | Thesis | |
| thesis.degree.discipline | ||
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Masters Theses | |
| thesis.degree.name | Master of Computer and Information Sciences | en_NZ |