Hybridization of Stochastic Local Search and Genetic Algorithm for Human Resource Planning Management

Škraba Andrej (), Stanovov Vladimir (), Semenkin Eugene () and Kofjač Davorin ()
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Škraba Andrej: University of Maribor, Faculty of Organizational Sciences, Kidričeva cesta 55a, SI-4000 Kranj, Slovenia
Stanovov Vladimir: Reshetnev Siberian State Aerospace University, Institute of Computer Science and Telecommunications, 31 »Krasnoyarskiy Rabochiy« ave., Krasnoyarsk, 660037, Russian Federation
Semenkin Eugene: Reshetnev Siberian State Aerospace University, Institute of Computer Science and Telecommunications, 31 »Krasnoyarskiy Rabochiy« ave., Krasnoyarsk, 660037, Russian Federation
Kofjač Davorin: University of Maribor, Faculty of Organizational Sciences, Kidričeva cesta 55a, SI-4000 Kranj, Slovenia

Organizacija, 2016, vol. 49, issue 1, 42-54

Abstract: Background and Purpose: The restructuring of human resources in an organization is addressed in this paper, because human resource planning is a crucial process in every organization. Here, a strict hierarchical structure of the organization is of concern here, for which a change in a particular class of the structure influences classes that follow it. Furthermore, a quick adaptation of the structure to the desired state is required, where oscillations in transitions between classes are not desired, because they slow down the process of adaptation. Therefore, optimization of such a structure is highly complex, and heuristic methods are needed to approach such problems to address them properly.Design/Methodology/Approach: The hierarchical human resources structure is modeled according to the principles of System Dynamics. Optimization of the structure is performed with an algorithm that combines stochastic local search and genetic algorithms.Results: The developed algorithm was tested on three scenarios; each scenario exhibits a different dynamic in achieving the desired state of the human resource structure. The results show that the developed algorithm has successfully optimized the model parameters to achieve the desired structure of human resources quickly.Conclusion: We have presented the mathematical model and optimization algorithm to tackle the restructuring of human resources for strict hierarchical organizations. With the developed algorithm, we have successfully achieved the desired organizational structure in all three cases, without the undesired oscillations in the transitions between classes and in the shortest possible time.

Keywords: stochastic local search; system dynamics; human resources; manpower; simulation (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:organi:v:49:y:2016:i:1:p:42-54:n:5

DOI: 10.1515/orga-2016-0005

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