ResearchActivities

Statistics, game theory and optimization

Statistics and optimization

The research group focuses on applications and theoretical results in statistics, optimization, fuzzy sets, and game theory. Our diverse topics are illustrated through collaborations with various institutes and institutions.

Selected Publications

With the Institute of Process Engineering:

  • Forecasting Waste Production: Eryganov et al. (2024) analyze hierarchical time series of waste production with a correlation structure.

  • Bilevel Programming: Eryganov et al. (2023) present bilevel programming methods in the price-setting game in waste-to-energy facilities.

  • Cost-effective Municipal Unions: The study by Eryganov et al. (2022) deals with forming economically efficient municipal unions prioritizing waste-to-energy.

  • Waste-to-energy plants’ price setting: Eryganov et al. (2021) focuses on the price-setting dynamics in waste-to-energy facilities.

  • AF2E-optim: Touš et al. proposed a system for optimizing the use of alternative fuels in heating plants considering economic and environmental aspects.

With the Institute of Automation and Computer Science:

  • Optimization of Transferred Graphene: Zahradníček et al. (2021) optimized the graphene transfer process using DOE methodology.

  • Machining of Titanium Alloy: Mouralová et al. (2016) focus on optimizing the machining of titanium alloy Ti-6Al-4V with an emphasis on surface quality.

With the Energy Institute:

  • Optimization of Charging Infrastructure: Cabalka et al. (2022) developed an optimization model for charging infrastructure for plug-in hybrids and battery electric vehicles.

  • Economic Optimization of Photovoltaic Systems: Cabalka et al. (2020) present a mathematical model for the economic optimization of a virtual photovoltaic plant with battery storage.

With the Institute of Aerospace Engineering:

  • Fuel Consumption Monitoring: Juračka et al. (2024) focus on monitoring the fuel consumption of a turboprop engine.

With the University of Defence:

  • ANN-FIS meta-model: Vališ et al. (2019) used this method to assess oil contamination in mechanical systems.

  • Fuzzy inference system: Vališ et al. (2019) applied fuzzy methods to optimize combustion engine maintenance.

  • Fuzzy logic prediction: Strejček et al. (2018) predicted the surface quality of machined parts during milling operation using fuzzy logic.

Theoretical

  • Perspective Exploratory Methods: Vališ et al. (2020) focused on applying these methods in multivariate analysis.

  • Partial Dependencies: Vališ et al. (2019) studied partial dependencies in multivariate data.

  • Forward-backward splitting algorithm: Benko, et al. (2024) derived the convergence rates of numerical method for granular medium equations using stochastic process methodology.

Collaboration with Companies

The research group collaborates closely with the private sector, including companies such as ERA, SC&C Partner, Wereldo, Nextbike s.r.o., CzechMath, VÚJE Czech Republic s.r.o., and EGÚ Brno.

Selected Outputs from Company Collaborations

  • Scheduling of Multi-Function Sensor: Kulmon et al. (2023) explore the scheduling of multifunction sensors (ERA).

  • Trace Optimization Software: Vašík et al. (2023) developed optimization software for route planning (Wereldo).

  • Monitoring system: Nehněvský et al. (2013) developed a utility model for early warning for operators (VÚJE Czech Republic s.r.o., Dukovany, CZ).

Some of these collaborations are/were supported by projects

  • THÉTA – TS01030053 Optimal design and control of energy systems using artificial intelligence

  • TREND – FW11020043 System for predictive optimization of flows in community energy

  • DOPRAVA 2030+ – CL01000161 Optimization of infrastructure and operation of micromobility in czech cities

  • OP PIK Aplikace – CZ.01.1.02/0.0/0.0/19_262/0020200 Advanced tracing software for various types of trucks

  • NPO – NPO_VUT_MSMT-16609/2022 Accelerating green skills and sustainability at BUT in Brno

  • TIRSMZP719 Forecasting waste production and determining the composition of municipal waste

  • Ministry of Agriculture of the Czech Republic – Applied Research Program ZEMĚ – QK1920344: Verification of honey authenticity using pollen grain analysisOvěření autenticity medu pomocí analýzy pylových zrn

  • TAČR NCK MESTEC – 02_DP_FSI NETME subproject within the project TN01000071 – National Competence Center for Mechatronics and Smart Technologies for Engineering

  • Internal project of BUT – Assessment of water ecosystem pollution by the most commonly used pharmaceuticals in the Czech Republic

Scientific Collaboration

Our scientific collaboration includes domestic and foreign institutions such as the Institute for State Control of Veterinary Biologicals and Medicines in Brno, Faculty of Business and Management, BUT, University of Defence, Norwegian School of Economics in Bergen, Molde University College – Specialized University in Logistics, University of Warsaw, and NTNU.

For more information about our projects, publications, and collaborations, visit our website. If you are interested in collaboration, feel free to contact us directly by email.

Members:
Ivan Eryganov, Pavel Hrabec, Zuzana Hübnerová, Zdeněk Karpíšek, Eva Mrázková, Pavel Popela, Libor Žák
Ph.D. students: Matej Benko, Matouš Cabalka

Assessment of Turboprop Engine Conditions through Statistical Analysis of Operational Parameters

In recent years, global manufacturers have implemented various systems for the continuous recording of operational parameters of aircraft engines. The primary goal is to monitor the condition of these engines effectively.

In the realm of aircraft engine behavior monitoring, we are increasingly relying on models that predict engine behavior or evaluate measured parameters. Advanced methods, such as neural networks that learn from vast amounts of data and statistical techniques that consider engine parameters and their interactions under varying conditions, have become essential tools.

Our objective is to develop a robust statistical approach for diagnosing and evaluating changes in the condition of turboprop aircraft engines. This approach aims to be versatile and applicable to a wide range of turboprop engines, potentially enhancing the reliability and efficiency of engine condition monitoring.

Contact:
Doc. Mgr. Zuzana Hubnerova, Ph.D.

Applications of game theory, optimization, and graph theory to waste management, logistics, and transportation.

Researchers at the Institute of Mathematics (IM) integrate game theory with mathematical programming to address complex decision-making processes in the context of sustainable waste management with a focus on prioritized waste energy recovery. Their research has contributed to the field by providing a cost-effective way for municipalities to form unions that can collectively manage waste and convert it into energy. This approach leads to efficient and financially sustainable waste energy recovery results from collaboration with researchers from the Norwegian School of Economics in Bergen, Norway. Their work involved the development of sophisticated prediction models based on game theory and quadratic programming to project future waste generation and optimize the waste management network operations. This collaboration resulted into certified methodology employed by Ministry of the Environment of the Czech Republic.

Contact:
Ing. Ivan Eryganov, Phd.