# American Institute of Mathematical Sciences

doi: 10.3934/jimo.2021149
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## Human resources optimization with MARS and ANN: Innovation geolocation model for generation Z

 1 Poznan University of Technology, Faculty of Engineering Management, Poznan, Poland 2 Warsaw University of Technology, Faculty of Geodesy and Cartography, Warsaw, Poland

* Corresponding author: Magdalena Graczyk-Kucharska E-mail address: magdalena.graczyk-kucharska@put.poznan.pl.

*ORCID IDs: Graczyk-Kucharska [0000-0002-4241-8216]; Robert Olszewski [0000-0003-1697- 9367]; Marek Golinski [0000-0002-0170-2835]; Ma lgorzata Spycha la [0000-0003-1471-5536]; Maciej Szafranski [0000-0002-4281-9845]; Gerhard Wilhelm Weber [0000-0003-0849-7771].

Received  March 2021 Revised  June 2021 Early access September 2021

Human resources (HR) have a key impact on the creation and implementation of modern products, solutions and concepts. Relatively new and rarely undertaken research challenge in enterprise is optimization of HR in the context of their location and requirements for working conditions. A great challenge here is the transparency and reliability of the collected data. In the article, we present a modern approach to knowledge extraction based on Artificial Intelligence (AI) and Multivariate Adaptive Regression Splines optimizing the availability of HR with a high innovation rate, taking into account their availability time and location. This study was conducted on a group of 5095 young people from the Z generation. A total of 11 variables were analyzed in the context of innovation and presented in this article. The effect of research using machine learning methods is the analysis of the characteristics of generation Z representatives, whose desire is to work in innovative companies. Research results indicate that some regions offer candidates with a higher level and commitment to innovation, and thus make HR more available for the development of innovative products. Chosen models designed by using AI and Operational Research Analytics were presented in the graphic visualization, which is a novelty in the presentation of similar issues in relation to HR.

Citation: Magdalena Graczyk-Kucharska, Robert Olszewski, Marek Golinski, Malgorzata Spychala, Maciej Szafranski, Gerhard Wilhelm Weber, Marek Miadowicz. Human resources optimization with MARS and ANN: Innovation geolocation model for generation Z. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2021149
##### References:

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##### References:
Distance zones from large cities (red circles – 30 km, pink circles – 60 km)
Model MLP for students graduating in 2018-2022
Model MLP for students graduating in 2018-2019
Model MLP for students graduating in 2020-2022
Dependent variable and independent variables selected for the innovation geolocation model
 Willingness of representatives of the Z generation to work in an innovative company $Y$ Gender: $X_1$ Year of graduation: $X_2$ Place of residence (close, average, far away from a large city): $X_3$ Unemployment in the region: $X_4$ Balance of migration in the region: $X_5$ Expected salary (high, medium, low): $X_6$ Willingness to continue university education: $X_7$ Willingness to leave the place of residence: $X_8$ Individual work vs. group work: $X_9$ Remote work vs. traditional work in the company: $X_{10}$ Work with passion vs. just work: $X_{11}$
 Willingness of representatives of the Z generation to work in an innovative company $Y$ Gender: $X_1$ Year of graduation: $X_2$ Place of residence (close, average, far away from a large city): $X_3$ Unemployment in the region: $X_4$ Balance of migration in the region: $X_5$ Expected salary (high, medium, low): $X_6$ Willingness to continue university education: $X_7$ Willingness to leave the place of residence: $X_8$ Individual work vs. group work: $X_9$ Remote work vs. traditional work in the company: $X_{10}$ Work with passion vs. just work: $X_{11}$
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