Učni načrt predmeta

Predmet:

Uporabne optimizacijske metode

Course:

Applied Optimization Methods

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Informacijske in komunikacijske tehnologije, 2. stopnja	/	1	1
Information and Communication Technologies, 2nd cycle	/	1	1

Vrsta predmeta / Course type

Izbirni / Elective

Univerzitetna koda predmeta / University course code:

IKT2-948

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
15	15			15	105	5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:

izr. prof. dr. Gregor Papa

Sodelavci / Lecturers:

Jeziki / Languages:

Predavanja / Lectures:

slovenščina, angleščina

Vaje / Tutorial:

Slovenian, English

Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:

Prerequisites:

Zaključen študij prve stopnje s področja naravoslovja, tehnike ali sorodnih področij ter osnovno znanje matematike in računalništva.

Completed first-cycle studies in science, engineering, or related fields, with basic knowledge of mathematics and computer science.

Vsebina:

Content (Syllabus outline):

Predmet pokriva celoten delovni tok reševanja optimizacijskih problemov: od identifikacije problema in njegove formulacije do implementacije v programskih knjižnicah ter interpretacije rezultatov. Študenti se bodo seznanili z modeliranjem optimizacijskih problemov v različnih aplikacijskih domenah, uporabo sodobnih programskih orodij in knjižnic ter tehnikami za analizo in validacijo rešitev. Obravnavane bodo praktične študije primerov iz različnih področij (npr. logistika, transport, načrtovanje proizvodnje, inženirstvo, energetski sistemi, omrežja) s poudarkom na implementaciji in interpretaciji rezultatov.

The course covers the complete workflow for solving optimization problems: from problem identification and formulation to implementation in software libraries and result interpretation. Students will become familiar with modeling optimization problems in various application domains, using modern software tools and libraries, and techniques for solution analysis and validation. Practical case studies from different fields (e.g. logistics, transportation, production planning, engineering, energy systems, networks) will be addressed with emphasis on implementation and result interpretation.

Temeljna literatura in viri / Readings:

Mykel J. Kochenderfer and Tim A. Wheeler (2019). Algorithms for Optimization. MIT Press.
Karin R. Saoub (2021). Graph Theory: An Introduction to Proofs, Algorithms, and Applications. Chapman and Hall/CRC. (Selected chapters)
Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer.
Dimitris Bertsimas and John N. Tsitsiklis (1997). Introduction to Linear Optimization. Athena Scientific. (Selected chapters)
Laurence A. Wolsey (2020). Integer Programming, 2nd ed. Wiley. (Selected chapters)
Selected documentation of optimization software libraries (e.g. Python-based tools).

Cilji in kompetence:

Objectives and competences:

Cilji predmeta so: (a) razviti razumevanje optimizacijskega modeliranja, (b) usposobiti študente za uporabo optimizacijskih metod in programskih knjižnic, (c) prikazati celovit pristop k reševanju praktičnih optimizacijskih problemov ter (d) razviti sposobnost kritične analize in interpretacije rešitev.

Študenti, ki bodo uspešno končali predmet, bodo sposobni oblikovati optimizacijske modele, jih implementirati z uporabo ustreznih programskih knjižnic ter kritično ovrednotiti dobljene rešitve.

The course objectives are to (a) develop an understanding of optimization modeling, (b) train students in the use of optimization methods and software libraries, (c) demonstrate an end-to-end approach to solving practical optimization problems, and (d) develop the ability to critically analyze and interpret solutions.

Students who successfully complete the course will be able to formulate optimization models, implement them using software libraries, and critically assess the obtained solutions.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Študenti bodo pridobili:
• sposobnost formulacije optimizacijskih problemov;
• sposobnost implementacije modelov v optimizacijskih knjižnicah;
• usposobljenost za uporabo programskih orodij pri reševanju praktičnih problemov;
• sposobnost interpretacije in vrednotenja rezultatov.

Students who successfully complete the course will acquire:
• ability to formulate optimization problems;
• ability to implement models using optimization software libraries;
• ability to use software tools for practical problem solving;
• ability to interpret and evaluate results.

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, programsko podprte vaje z učenjem uporabe optimizacijskih knjižnic, reševanje praktičnih problemov, konzultacije in samostojno delo.

Lectures, software-supported exercises focused on learning optimization libraries, practical problem solving, consultations, and individual work.

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Seminarska naloga (modeliranje in programska realizacija)

50 %

Seminar work (modeling and software implementation)

Ustni zagovor seminarske naloge

50 %

Oral defense of seminar work

Reference nosilca / Lecturer's references:

1.	Margarita Antoniou, Drago Torkar, et al. (2025). "Differential evolution for maximum cuboid extraction in sustainable stone cutting: Application to a granite rock example." Revista Minelor – Mining Revue, vol. 31.2.
2.	Margarita Antoniou and Peter Korošec (2021). "Multilevel Optimisation." In: Optimization Under Uncertainty with Applications to Aerospace Engineering, pp. 307–331.
3.	Margarita Antoniou, Gašper Petelin, and Gregor Papa (2020). "On formulating the ground scheduling problem as a multi-objective bilevel problem." In: Bioinspired Optimization Methods and Their Applications: 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020. Springer International Publishing, pp. 177–188.
4.	Margarita Antoniou, Ankur Sinha, and Gregor Papa (2024). "δ-perturbation of bilevel optimization problems: An error bound analysis." Operations Research Perspectives, p. 100315.
5.	Margarita Antoniou and Gregor Papa (2021). “Di!erential evolution with estimation of distribution for worst-case scenario optimization”. In: Mathematics 9.17, p. 2137