<abstract xmlns="http://www.w3.org/1999/xhtml"> 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.</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.

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

University of Maribor, Faculty of Organizational Sciences, Kidričeva cesta 55a, SI-4000 Kranj, Slovenia

Reshetnev Siberian State Aerospace University, Institute of Computer Science and Telecommunications, 31 »Krasnoyarskiy Rabochiy« ave., Krasnoyarsk, 660037, Russian Federation

Organizacija

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Background and Purpose: The restructuring of human resources in an organization is addressed in this paper, because human resource planning is a crucial...

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

Published Online: Mar 10, 2016

Page range: 42 - 54

Received: Aug 14, 2015

Accepted: Nov 15, 2015

DOI: https://doi.org/10.1515/orga-2016-0005

Keywords
stochastic local search, system dynamics, human resources, manpower, simulation

© by Andrej Škraba

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Published Online: Mar 10, 2016

Page range: 42 - 54

Received: Aug 14, 2015

Accepted: Nov 15, 2015

DOI: https://doi.org/10.1515/orga-2016-0005

Keywordsstochastic local search, system dynamics, human resources, manpower, simulation

© by Andrej Škraba

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
stochastic local search, system dynamics, human resources, manpower, simulation