Učni načrt predmeta

Predmet:

Napredni pristopi planiranja in razvrščanja

Course:

Advanced Approaches to Planning and Scheduling

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Informacijske in komunikacijske	Računalniške strukture in sistemi	1	1
Information and Communication	Computer Structures and Systems	1	1

Vrsta predmeta / Course type

Izbirni / Elective

Univerzitetna koda predmeta / University course code:

IKT3-702

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 / Slovenian, English

Vaje / Tutorial:

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

Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second cycle studies in information or communication technologies or completed second cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:

Content (Syllabus outline):

Uvod:
Terminologija in definicije

Planiranje in razvrščanje:
Hierarhičnost, zahteve in omejitve, osnovni pristopi (hevristični, deterministični, stohastični)

Področja uporabe:
Proizvodnja, logistika/transport, upravljanje virov

Modeliranje problemov:
Zahtevnost, upoštevanje omejitev, večkriterijskost, večnivojskost

Integrirano planiranje in razvrščanje:
Značilnosti, pristopi, izvedbe

Planiranje in razvrščanje v spremenljivih okoljih in v negotovih pogojih:
Vpliv na modeliranje, izvedbo, natančnost

Napredni algoritmi planiranja in razvrščanja: Tehnike, izvedbe, računska zahtevnost, časovna odzivnost, natančnost, zanesljivost, (samo)-prilagodljivost

Analiza učinkovitosti algoritmov:
Načrtovanje eksperimenta, mere učinkovitosti, analiza rezultatov, predstavitev rezultatov.

Introduction:
Terminology and Definitions

Planning and Scheduling:
Hierarchy, requirements and constraints, basic approaches (heuristic, deterministic, stochastic)

Application Fields:
Production, logistics/transport, resource management

Problem Modeling:
Complexity, constraints handling, multi-criteria, multi-level

Integrated Planning and Scheduling:
Characteristics, approaches, applications

Planning and Scheduling in Dynamic Environments and Uncertain Conditions:
Influence on modeling, application, accuracy

Advanced Planning and Scheduling Algorithms:
Techniques, applications, computational complexity, response time, accuracy, reliability, (self)-adaptability

Algorithms’ performance analysis:
Experimental design, performance metrics, results analysis, results presentation

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:
- J.M. Framinan, R. Leisten, and R.R. García, Manufacturing Scheduling Systems: An Integrated View on Models, Methods and Tools. Springer, 2014, ISBN: 978-1-4471-6271-1.
- M.L. Pinedo, Planning and Scheduling in Manufacturing and Services. Springer, 2007. ISBN: 978-0387221984.
- C. Jaffeux, and P. Wieser (eds.), Essentials of Logistics and Management. EPFL Press 2012. ISBN: 978-1466573086.
- A. Ceder, Public Transit Planning and Operation: Theory, Modeling, and Practice. CRC Press, 2007. ISBN: 978-0750661669.
- T. Bartz-Beielstein et al., Benchmarking in Optimization: Best Practice and Open Issues. arXiv:2007.03488v1, 2020.
- T. Eftimov, P. Korošec, Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms, Springer, 2022, ISBN: 978-3-030-96917-2.

Cilji in kompetence:

Objectives and competences:

Cilj predmeta je seznaniti študenta s pristopi planiranja in razvrščanja za različne tipe proizvodnih in transportnih/logističnih operacij/sistemov s poudarkom na naprednih algoritmih računske inteligence.

Kompetence študenta z uspešno zaključenim predmetom bodo vključevale razumevanje in sposobnost zasnove in obravnave zahtevnih problemov planiranja/razvrščanja na različnih hierarhičnih nivojih ter pridobitev vedenja o zasnovi in izvedbi sistemov za napredno planiranje in razvrščanje ter analizo učinkovitosti njihovega delovanja, tako na področjih proizvodnje in/ali transporta.

The goal of the course is to familiarize the student with the methods of planning and scheduling for different types of manufacturing and transportation/logistic operations/systems, where the stress is on advanced algorithms of
computational intelligence.

The competencies of the students completing this course successfully would include understanding and ability to formulate and manage complex planning/scheduling problems at different hierarchical levels, and be knowledgeable about the design and application of systems for advanced planning and scheduling, and their performance analysis in the fields of manufacturing and/or transportation.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- znanstveno védenje o formuliranju in analizi hierarhičnih problemov planiranja in razvrščanja
- znanstvene aktivnosti, kot je modeliranje zahtevnih problemov planiranja/razvrščanja
- pregled obstoječih nalog in metod planiranja in razvrščanja
- pregled obstoječih nalog in metod računske inteligence
- sposobnost uporabe obstoječih metod na drugih področjih, kjer so potrebni pristopi planiranja in razvrščanja
- sposobnost analize učinkovitosti delovanja algoritmov

Students successfully completing this course will acquire:
- Scientific knowledge on formulating and analysing hierarchical planning and scheduling problems
- Scientific activities, such as modelling complex planning/scheduling problems
- Overview of existing tasks and methods of planning and scheduling
- Overview of existing tasks and methods of computational intelligence
- The ability to apply existing methods to other fields that require efficient planning and scheduling approaches
- The ability to analyse algorithms performance

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo.

Lectures, seminar, consultations, individual work.

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Seminarska naloga

50 %

Seminar work

Ustni zagovor seminarske naloge

50 %

Oral defense of seminar work

Reference nosilca / Lecturer's references:

1.	PETELIN, Gašper, HRIBAR, Rok, PAPA, Gregor. Models for forecasting the traffic flow within the city of Ljubljana. European transport research review. 2023, vol. 15, article no. 30, pp. 1-20, DOI: 10.1186/s12544-023-00600-6.
2.	PAPA, Gregor, SANTO-ZARNIK, Marina, VUKAŠINOVIĆ, Vida. Electric-bus routes in hilly urban areas: overview and challenges. Renewable & sustainable energy reviews, 2022, vol. 165, pp. 112555-1-112555-19, DOI: 10.1016/j.rser.2022.112555.
3.	PETELIN, Gašper, ANTONIOU, Margarita, PAPA, Gregor. Multi-objective approaches to ground station scheduling for optimization of communication with satellites. Optimization and engineering. 2021, vol. 24, pp. 147–184, DOI: 10.1007/s11081-021-09617-z.
4.	ANTONIOU, Margarita, PETELIN, Gašper, PAPA, Gregor. Preferred solutions of the ground station scheduling problem using NSGA-III weighted reference points selection. In: 2021 IEEE Congress on Evolutionary Computation, IEEE CEC 2021, June 28th and July 1st 2021, Kraków, Poland. IEEE, 2021. pp. 1840-1847.
5.	PAPA, Gregor. Applications of dynamic parameter control in evolutionary computation. In: CHICANO, Francisco (ur.). GECCO '21, Proceedings of the Genetic and Evolutionary Computation Conference Companion, 10 July - 14 July, 2021, Lille, France. ACM, 2021. Str. 1064-1088, DOI: 10.1145/3449726.3461435.