PROPOSITION OF A MATHEMATICAL PROGRAMMING MODEL FOR ALLOCATING HUMAN RESOURCES CONSIDERING MULTIPLE FACTORS AND USING DIFFERENT HEURISTICS
ABSTRACT Human Resource Allocation (HRA) can be defined as the way professionals are distributed across the organization’s tasks, given that each individual has his/her own set of characteristics, and that each task has specific needs. Thus, this paper puts forward a mathematical programming model for allocating human resources that considers employees’ formal qualifications and experience and the possibility of employees sharing tasks in each project. The proposed mathematical model was designed and implemented according to a set of heuristics based on a Greedy Search (GS), a Genetic Algorithm, a Cosine Pigeon- Inspired Optimizer and an Iterated Local Search (ILS), to solve small, medium and large random instances. Thus, it was verified which of the heuristics had the best performance according to certain indicators, such as resolution time and average quality of the solutions found. Finally, they were also compared with the optimal solution obtained for small and medium-sized instances, with the best average results to ILS, although these are not too far from those of the GS.
| Main Authors: | , , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Sociedade Brasileira de Pesquisa Operacional
2022
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| Online Access: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382022000100205 |
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| Summary: | ABSTRACT Human Resource Allocation (HRA) can be defined as the way professionals are distributed across the organization’s tasks, given that each individual has his/her own set of characteristics, and that each task has specific needs. Thus, this paper puts forward a mathematical programming model for allocating human resources that considers employees’ formal qualifications and experience and the possibility of employees sharing tasks in each project. The proposed mathematical model was designed and implemented according to a set of heuristics based on a Greedy Search (GS), a Genetic Algorithm, a Cosine Pigeon- Inspired Optimizer and an Iterated Local Search (ILS), to solve small, medium and large random instances. Thus, it was verified which of the heuristics had the best performance according to certain indicators, such as resolution time and average quality of the solutions found. Finally, they were also compared with the optimal solution obtained for small and medium-sized instances, with the best average results to ILS, although these are not too far from those of the GS. |
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