Učni načrt predmeta

Predmet:
Napredno optimizacijsko modeliranje in metode
Course:
Advanced Optimization Modelling and Methods
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja / 1 1
Information and Communication Technologies, 3rd cycle / 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-942
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 druge stopnje s področja optimiranja, matematike, računalništva, tehnike ali sorodnih področij ter osnovno znanje optimizacijskih metod.

Completed second-cycle studies in optimization, mathematics, computer science, engineering or related fields, with basic knowledge of optimization methods.

Vsebina:
Content (Syllabus outline):

Študenti se bodo seznanili z naprednimi optimizacijskimi formulacijami, ki se pojavljajo v sodobnih raziskavah in aplikacijah. Predmet zagotavlja sistematičen pregled problemskih struktur, formulacijskih paradigem in pristopov k reševanju v različnih razredih optimizacijskih problemov.

Obravnavane bodo različne problemske strukture, vključno z večnivojskimi in hierarhičnimi formulacijami, problemi z negotovostjo, več-ciljinimi formulacijami, diskretno-zveznimi problemi, implicitnimi in črno-škatljastimi strukturami, dinamičnimi in časovnimi problemi ter specializiranimi omejitvami. Za vsak razred problemov bodo predstavljeni pristopi k reševanju.

Poudarek je na prepoznavanju in klasifikaciji problemov z uporabo uveljavljenih taksonomij, razumevanju povezav med značilnostmi problema in ustreznimi formulacijskimi paradigmami ter razvoju sistemskih pristopov k izbiri formulacij. Študenti bodo analizirali raziskovalno literaturo za razumevanje vpliva formulacijskih izbir na metode reševanja in raziskovalne rezultate.

Cilj je zagotoviti celovit pregled optimizacijskih formulacij, kar študentom omogoča prepoznavanje problemskih struktur v lastnih raziskavah, izbiro ustreznih modelirnih paradigem in učinkovito formuliranje kompleksnih odločitvenih problemov v različnih aplikacijskih domenah.

Students will be introduced to advanced optimization formulations encountered in contemporary research and applications. The course provides a systematic overview of problem structures, formulation paradigms, and solution approaches across diverse optimization problem classes.

Topics cover various problem structures including multilevel and hierarchical formulations, problems with uncertainty, multiobjective formulations, discrete-continuous problems, implicit and black-box structures, dynamic and temporal problems, and specialized constraints. For each problem class, solution approaches will be presented.

The course emphasizes problem recognition and classification using established taxonomies and frameworks, understanding relationships between problem characteristics and appropriate formulation paradigms, and developing systematic approaches to formulation selection. Students will analyze research literature to understand how formulation choices impact solution methods and research outcomes.

The goal is to provide comprehensive exposure to optimization formulations, enabling students to recognize problem structures in their research, select appropriate modeling paradigms, and effectively formulate complex decision problems across different application domains.

Temeljna literatura in viri / Readings:

• Mykel J Kochenderfer and Tim A Wheeler (2019). Algorithms for optimization. Mit Press
• Jonathan F Bard (2013). Practical bilevel optimization: algorithms and applications. Vol. 30. Springer Science & Business Media
• A. Ben-Tal, L. El Ghaoui, and A. Nemirovski (2009). Robust Optimization. Princeton University Press
• Selected research articles from international journals (Mathematical Programming, Operations Research, SIAM Journal on Optimization, European Journal of Operational Research, Optimization and Engineering) and conference proceedings in optimization and related fields.

Cilji in kompetence:
Objectives and competences:

Cilj predmeta je pridobiti celovit pregled naprednih optimizacijskih formulacij ter razviti sposobnost prepoznavanja, klasifikacije in ustreznega formuliranja optimizacijskih problemov, ki se pojavljajo v raziskovalnih kontekstih.

Pomemben cilj je razumeti pokrajino optimizacijskih formulacij, kar študentom omogoča umestitev konkretnih raziskovalnih problemov v širši optimizacijski okvir in učinkovito komunikacijo z raziskovalci različnih aplikacijskih področij. Študenti bodo razvili kompetence pri izbiri ustreznih formulacijskih paradigem glede na značilnosti problema ter razumevanje vplivov formulacijskih izbir na pristope k reševanju.

The aim of the course is to provide a comprehensive overview of advanced optimization formulations and to develop the ability to recognize, classify, and appropriately formulate optimization problems encountered in research contexts.

An important goal is to understand the landscape of optimization formulations, enabling students to place their specific research problems within the broader optimization framework and to effectively communicate with researchers across different application domains. Students will develop competence in selecting appropriate formulation paradigms based on problem characteristics and understanding the implications of formulation choices for solution approaches.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo pridobili:
• celovit pregled naprednih optimizacijskih formulacij;
• sposobnost prepoznavanja in klasifikacije problemskih struktur;
• razumevanje povezav med formulacijskimi izbirami in pristopi k reševanju;
• sposobnost samostojne formulacije raziskovalnih problemov;
• usposobljenost za kritično analizo formulacijskih izbir v znanstveni literaturi;
• sposobnost integracije optimizacijskih pristopov preko različnih raziskovalnih področij.

Students successfully completing this course will acquire:
• comprehensive overview of advanced optimization formulations;
• ability to recognize and classify problem structures;
• understanding of relationships between formulation choices and solution approaches;
• ability to independently formulate research problems;
• competence in critically analyzing formulation choices in scientific literature;
• ability to integrate optimization approaches across different research areas.

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar z aktivno udeležbo študentov, razprava znanstvenih člankov, samostojno raziskovalno delo.

Lectures, seminar with active student participation, discussion of research papers, independent research work.

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Raziskovalna seminarska naloga
50 %
Research seminar paper
Ustna predstavitev in zagovor
50 %
Oral presentation and defense
Reference nosilca / Lecturer's references:
1. • Margarita Antoniou and Peter Korošec (2021). "Multilevel Optimisation". In: Optimization Under Uncertainty with Applications to Aerospace Engineering, pp. 307–331
2. • 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, Proceedings 9. Springer International Publishing, pp. 177–188
3. • Margarita Antoniou, Ankur Sinha, and Gregor Papa (2024). "ω-perturbation of bilevel optimization problems: An error bound analysis". In: Operations Research Perspectives, p. 100315
4. • Margarita Antoniou, N Theodossiou, Diamandis Karakatsanis, et al. (2017). "Coupling groundwater simulation and optimization models, using MODFLOW and Harmony Search Algorithm". In: Desalin Water Treat 86, pp. 297–304
5. • Margarita Antoniou and Gregor Papa (2021). "Differential evolution with estimation of distribution for worst-case scenario optimization". In: Mathematics 9.17, p. 2137