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Title:Primerjava klasičnega in inteligentnega terminiranja proizvodnje z umetno inteligenco v podjetju RaMaX engineering d. o. o. : magistrsko delo
Authors:ID Rojc, Špela (Author)
ID Palčič, Iztok (Mentor) More about this mentor... New window
ID Nedelko, Zlatko (Mentor) More about this mentor... New window
ID Brezočnik, Miran (Comentor)
ID Martinšek, Emil (Comentor)
Files:.pdf MAG_Rojc_Spela_2026.pdf (2,65 MB)
MD5: 98D92E0C3ADEBDE7B9A284D515D63F74
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:V magistrskem delu je predstavljena primerjava klasičnega terminiranja proizvodnje s sodobnimi pristopi z uporabo umetne inteligence. Glavni cilj naloge je bil preučiti uporabo genetskega algoritma pri terminiranju proizvodnje v primerjavi s klasičnimi pristopi. Za razvoj kode genetskega algoritma je bil uporabljen programski jezik Python. Rezultati pokažejo, da umetna inteligenca ni zamenjava za človeka in njegovo delo, temveč zelo koristno in uporabno orodje, s katerim si lahko delo olajšamo. Z uporabo genetskega algoritma pri terminiranju proizvodnje se v primerjavi s klasičnimi pristopi skrajša čas izdelave terminskega plana ter poveča izkoriščenost strojev.
Keywords:terminiranje proizvodnje, umetna inteligenca, genetski algoritem, primerjava klasičnih in sodobnih metod
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[Š. Rojc]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XI, 84 f.))
PID:20.500.12556/DKUM-96377 New window
UDC:658.512/.514:004.8(043.2)
COBISS.SI-ID:266676739 New window
Publication date in DKUM:27.01.2026
Views:403
Downloads:66
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:23.12.2025

Secondary language

Language:English
Title:Comparison of Classical and Intelligent Production scheduling with Artificial Intelligence at RaMaX Engineering d. o. o.
Abstract:This master’s thesis presents a comparison between classical production scheduling and modern approaches using artificial intelligence. The main objective of the thesis was to examine the use of a genetic algorithm for production scheduling in comparison with classical methods. The Python programming language was used to develop the genetic algorithm code. The results show that artificial intelligence is not a replacement for humans and their work, but rather a very useful tool that can significantly support and simplify work processes. The use of a genetic algorithm for production scheduling reduces the time required to generate the schedule and increases machine utilization compared to classical approaches.
Keywords:production scheduling, artificial intelligence, genetic algorithm, comparison of classical and modern methods


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