Evolutionary algorithm for analyzing higher degree research student recruitment and completion
In this paper, we consider a decision problem arising from higher degree research student recruitment process in a university environment. The problem is to recruit a number of research students by maximizing the sum of a performance index satisfying a number of constraints, such as supervision capa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2015-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2015.1063760 |
| _version_ | 1797761840729554944 |
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| author | Ruhul Sarker Saber Elsayed |
| author_facet | Ruhul Sarker Saber Elsayed |
| author_sort | Ruhul Sarker |
| collection | DOAJ |
| description | In this paper, we consider a decision problem arising from higher degree research student recruitment process in a university environment. The problem is to recruit a number of research students by maximizing the sum of a performance index satisfying a number of constraints, such as supervision capacity and resource limitation. The problem is dynamic in nature as the number of eligible applicants, the supervision capacity, completion time, funding for scholarships, and other resources vary from period to period and they are difficult to predict in advance. In this research, we have developed a mathematical model to represent this dynamic decision problem and adopted an evolutionary algorithm-based approach to solve the problem. We have demonstrated how the recruitment decision can be made with a defined objective and how the model can be used for long-run planning for improvement of higher degree research program. |
| first_indexed | 2024-03-12T19:19:51Z |
| format | Article |
| id | doaj.art-60a016068d314b7dab5ce978018d3a32 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T19:19:51Z |
| publishDate | 2015-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-60a016068d314b7dab5ce978018d3a322023-08-02T05:16:34ZengTaylor & Francis GroupCogent Engineering2331-19162015-12-012110.1080/23311916.2015.10637601063760Evolutionary algorithm for analyzing higher degree research student recruitment and completionRuhul Sarker0Saber Elsayed1University of New South Wales at CanberraUniversity of New South Wales at CanberraIn this paper, we consider a decision problem arising from higher degree research student recruitment process in a university environment. The problem is to recruit a number of research students by maximizing the sum of a performance index satisfying a number of constraints, such as supervision capacity and resource limitation. The problem is dynamic in nature as the number of eligible applicants, the supervision capacity, completion time, funding for scholarships, and other resources vary from period to period and they are difficult to predict in advance. In this research, we have developed a mathematical model to represent this dynamic decision problem and adopted an evolutionary algorithm-based approach to solve the problem. We have demonstrated how the recruitment decision can be made with a defined objective and how the model can be used for long-run planning for improvement of higher degree research program.http://dx.doi.org/10.1080/23311916.2015.1063760Higher degree research student recruitmentdifferential evolutionevolutionary algorithms |
| spellingShingle | Ruhul Sarker Saber Elsayed Evolutionary algorithm for analyzing higher degree research student recruitment and completion Cogent Engineering Higher degree research student recruitment differential evolution evolutionary algorithms |
| title | Evolutionary algorithm for analyzing higher degree research student recruitment and completion |
| title_full | Evolutionary algorithm for analyzing higher degree research student recruitment and completion |
| title_fullStr | Evolutionary algorithm for analyzing higher degree research student recruitment and completion |
| title_full_unstemmed | Evolutionary algorithm for analyzing higher degree research student recruitment and completion |
| title_short | Evolutionary algorithm for analyzing higher degree research student recruitment and completion |
| title_sort | evolutionary algorithm for analyzing higher degree research student recruitment and completion |
| topic | Higher degree research student recruitment differential evolution evolutionary algorithms |
| url | http://dx.doi.org/10.1080/23311916.2015.1063760 |
| work_keys_str_mv | AT ruhulsarker evolutionaryalgorithmforanalyzinghigherdegreeresearchstudentrecruitmentandcompletion AT saberelsayed evolutionaryalgorithmforanalyzinghigherdegreeresearchstudentrecruitmentandcompletion |