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Projekta vadītājs/a:
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Academic degree and current position in TSI: assistant professor, researcher at the research cluster “Data Analytics and Artificial Intelligence
Previous experience: 4 years of lecturing and research at the Transport and Telecommunication Institute (TSI)
Membership: Reviewer of the Hawaii International Conference on System Sciences (HICSS) and Transport Journal. Member of the scientific committee of the International Scientific Conference “Transbaltica 2021”. Member of a working group European Transport Research Review (ECTRI), thematic group “freight & logistics”.
Academic experience: More than 13 publications indexed by SCOPUS and WoS. Author of such courses as “Risk management in supply chains”, “Modeling Logistic and transport processes”, “Logistic systems and chains”.
Teaching at post- and graduate level: Risk management in supply chains (MSc in Transport and Logistics), Modeling Logistic and transport processes (MSc in Transport and Logistics), Logistic systems and chains (MSc in Transport and Logistics).
Participation in projects: participated as a researcher in several European projects including ALLIANCE (ID: 692426) and ePIcenter (ID: 861584).
Research Interests: Supply chain management, applied machine learning, simulation modelling, operations research and metaheuristics.
Supervised Doctoral, Master and Bachelor Theses (number): 1 master student completed master’s degree under my supervision.
Awards: Young Researcher Award was received at the 20th International Multi-Conference Reliability and Statistics in Transportation and Communication, 2020, Riga, Latvia. Awarded paper: “Neuroevolutionary approach to metamodel-based optimization in production and logistics”
Projekta tips: Lietišķais
Galvenais izaicinājums:
Vidējā krājumu sistēmā ir milzīgs krājumu uzturēšanas vienību (SKU) skaits. Parasti skaitļošanas ziņā nav iespējams katru priekšmetu izskatīt atsevišķi un pārvaldīt saskaņā ar individuālo krājumu politiku. Šī projekta mērķis ir attīstīt bez uzraudzības mācību pieeju krājumu uzturēšanas vienību segmentēšanas problēmas risināšanai. Tā kā “pamata patiesība” nav zināma, pētījuma mērķis ir salīdzināt tādus klasteru algoritmus kā K-nozīme, vidējā nobīde un DBSCAN, pamatojoties tikai uz iekšējo novērtējumu, tādējādi šo pētījumu var uzskatīt par aprakstošu klasteru analīzi.
Finansējums: VIAA stipendija doktora studijām, lēmums Nr.1.-50.3/3889
DA&AI rīki:
Rezultāti (publikācijas, ziņojumi, apliecinājumi utt.)
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