Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
|
|
SLO
|
ENG
|
Cookies and privacy
DKUM
EPF - Faculty of Business and Economics
FE - Faculty of Energy Technology
FERI - Faculty of Electrical Engineering and Computer Science
FF - Faculty of Arts
FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
FKBV - Faculty of Agriculture and Life Sciences
FKKT - Faculty of Chemistry and Chemical Engineering
FL - Faculty of Logistic
FNM - Faculty of Natural Sciences and Mathematics
FOV - Faculty of Organizational Sciences in Kranj
FS - Faculty of Mechanical Engineering
FT - Faculty of Tourism
FVV - Faculty of Criminal Justice and Security
FZV - Faculty of Health Sciences
MF - Faculty of Medicine
PEF - Faculty of Education
PF - Faculty of Law
UKM - University of Maribor Library
UM - University of Maribor
UZUM - University of Maribor Press
COBISS
Faculty of Business and Economic, Maribor
Faculty of Agriculture and Life Sciences, Maribor
Faculty of Logistics, Celje, Krško
Faculty of Organizational Sciences, Kranj
Faculty of Criminal Justice and Security, Ljubljana
Faculty of Health Sciences
Library of Technical Faculties, Maribor
Faculty of Medicine, Maribor
Miklošič Library FPNM, Maribor
Faculty of Law, Maribor
University of Maribor Library
Bigger font
|
Smaller font
Introduction
Search
Browsing
Upload document
For students
For employees
Statistics
Login
First page
>
Show document
Show document
Title:
Učinkovitost algoritmov umetne inteligence pri mikroplaniranju proizvodnje
Authors:
ID
Radisavljević, Vukašin
(Author)
ID
Roblek, Matjaž
(Mentor)
More about this mentor...
ID
Brezavšček, Alenka
(Comentor)
Files:
UN_Radisavljevic_Vukasin_2024.pdf
(4,53 MB)
MD5: 32C08AFA8C32197A097BA25F0373E4D9
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FOV - Faculty of Organizational Sciences in Kranj
Abstract:
Diplomsko delo se osredotoča na analizo podatkov v kontekstu uporabe umetne inteligence pri mikroplaniranju proizvodnje. Na podlagi analize pridobljenih podatkov smo identificirali zakonitosti in trende, ki se nanašajo na učinkovitost sistema za napredno planiranje in razporejanje proizvodnje z umetno inteligenco Qlector LEAP. Opažamo korelacije med relativno napako planiranja s Qlector LEAP-om in številom poskusov planiranja, pri čemer opažamo določene trende za določene izdelke. Primerjamo učinek planiranja Qlector LEAP-a tudi z učinkom planiranja po normativih. Razprava se osredotoča tudi na tehnološke, kadrovske in organizacijske dejavnike ter priporoča organizacijske ukrepe za izboljšanje učinkovitosti planiranja z LEAP-om. Kljub izzivom pri dokazovanju hipotez je razprava pokazala možnosti za nadaljnje raziskave, ki vključujejo kvantifikacijo zanesljivosti planiranja z LEAP-om in preučevanje drugih modulov Qlector LEAP-a. Skupaj s postavljenimi organizacijskimi ukrepi diplomsko delo zagotavlja osnovo za nadaljnje raziskave na tem področju.
Keywords:
umetna inteligenca
,
strojno učenje
,
mikroplaniranje proizvodnje
,
sistem za napredno planiranje in razporejanje proizvodnje
,
merjenje učinka
Place of publishing:
Kranj
Year of publishing:
2024
PID:
20.500.12556/DKUM-88599
COBISS.SI-ID:
198758915
Publication date in DKUM:
13.06.2024
Views:
189
Downloads:
28
Metadata:
Categories:
FOV
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
RADISAVLJEVIĆ, Vukašin, 2024,
Učinkovitost algoritmov umetne inteligence pri mikroplaniranju proizvodnje
[online]. Bachelor’s thesis. Kranj. [Accessed 20 January 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=88599
Copy citation
Average score:
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
(0 votes)
Your score:
Voting is allowed only for
logged in
users.
Share:
Searching for similar works...
Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.
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:
15.05.2024
Secondary language
Language:
English
Title:
Performance of ai algorithms in production scheduling
Abstract:
The thesis focuses on data analysis in the context of artificial intelligence usage in production scheduling. Based on the analysis of the acquired data, we identified patterns and trends related to the effectiveness of the APS system with artificial intelligence (Qlector LEAP). We observe correlations between the relative planning error with Qlector LEAP and the number of planning attempts, where we notice certain trends for specific products. Additionally, we compare the planning efficiency of Qlector LEAP with the efficiency of planning based on normative times. The discussion also emphasizes technological, personnel, and organizational factors, recommending organizational measures to improve planning efficiency with LEAP. Despite challenges in proving hypotheses, the discussion has shown possibilities for further research, including quantifying the reliability of planning with LEAP and examining other Qlector LEAP modules. Together with the proposed organizational measures, the thesis provides a solid foundation for further research in this field.
Keywords:
artificial inteligence
,
machine learning
,
production scheduling
,
APS system
,
performance assesment
Comments
Leave comment
You must
log in
to leave a comment.
Comments (0)
0 - 0 / 0
There are no comments!
Back