Hybridization of stochastic local search and genetic algorithm for human resource planning management

Andrej Škraba (Author), Vladimir Stanovov (Author), Eugene Semenkin (Author), Davorin Kofjač (Author)

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;simulation;

Data

Language:	English
Year of publishing:	2016
Typology:	1.01 - Original Scientific Article
Organization:	UM FOV - Faculty of Organizational Sciences
UDC:	004.94
COBISS:	7526419
ISSN:	1318-5454
Parent publication:	Organizacija
Views:	723
Downloads:	300
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Hibridizacija stohastičnega lokalnega iskanja in genetskih algoritmov za planiranje človeških virov
Secondary abstract:	Ozadje in cilj: V prispevku je obravnavana reorganizacija na področju človeških virov kot najpomembnejšega dejavnika v vsaki organizaciji. Obravnavali smo striktno hierarhično strukturo organizacije, kjer spremembe v posameznem nižjem razredu vplivajo na višje razrede. Pri reorganizaciji želimo, da se struktura čim prej prilagodi novim, želenim vrednostim. Pri tem so nihanja v številu prehodov nezaželena, saj neugodno vplivajo na proces reorganizacije. Optimizacija tovrstne strukture je kompleksna in zahteva ustrezen pristop s hevrističnimi metodami. Metodologija in pristop: Hierarhična struktura človeških virov v organizaciji je modelirana s pomočjo principov sistemske dinamike. Optimizacija dinamike obravnavane strukture je izvedena z algoritmom, ki kombinira stohastično lokalno iskanje in genetske algoritme. Rezultati: Razviti algoritem je bil testiran na treh različnih scenarijih; vsak od scenarijev je izkazoval drugačno dinamiko pri doseganju želenih stanj v strukturi človeških virov. Rezultati so potrdili uspešnost razvitega algoritma za optimizacijo parametrov modela, ki omogoča hitro doseganje ciljnih stanj. Zaključek: Predstavili smo matematični model in optimizacijski algoritem, ki omogoča prestrukturiranje na področju človeških virov v organizacijah. S pomočjo razvitega algoritma smo uspešno dosegli želeno organizacijsko strukturo v treh različnih podanih scenarijih brez nezaželenih oscilacij v številu prehodov.
Secondary keywords:	stohastično lokalno iskanje;sistemska dinamika;človeški viri;simulacija;
URN:	URN:NBN:SI
Type (COBISS):	Scientific work
Pages:	str. 42-54
Volume:	ǂVol. ǂ49
Issue:	ǂno. ǂ1
Chronology:	feb. 2016
DOI:	10.1515/orga-2016-005
ID:	9599680