700.372 (22S) Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
Überblick
Weitere Informationen zum Lehrbetrieb vor Ort finden Sie unter: https://www.aau.at/corona.
- Lehrende/r
- LV-Titel englisch Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
- LV-Art Kurs (prüfungsimmanente LV )
- LV-Modell Onlinelehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 6 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 08.03.2022
- eLearning zum Moodle-Kurs
- Seniorstudium Liberale Ja
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
This lecture is subdivided into two parts:
PART 1. This part familiarizes students with the fundamentals of optimization and neural networks. Selected applications are considered in various fields of engineering including transportation.
The general expectation regarding the knowledge to be provided/acquired is as follows:
- Mastering of the basics of optimization and selected applications
- Mastering of some MATLAB Toolboxes (e.g. Linear programming and Quadratic programming toolboxes) and their application in solving linear and nonlinear optimization problems.
- Mastering of Recurrent Neural Networks and their application in solving linear and nonlinear optimization problems.
- Mastering of the development of simulation algorithms (based on Recurrent Neural Networks) for the solving of shortest path problems and traveling salesman problems in graph networks.
Lehrmethodik inkl. Einsatz von eLearning-Tools
The slides are available for the entire lecture. These slides are uploaded into the MOODLE system. The entire content of each slide is systematically explained by the lecturer.
Additional examples that are not included in the slides are suggested by the lecturer to allow a good understanding of the information provided.
The slides contain exercises with solutions to allow a good understanding of the contents of each chapter. These solutions are systematically explained (during the lecture) by the lecturer.
The Lecturer provides full explanation of how to write numerical codes to solve the exercises proposed in each chapter of the Lecture.
Inhalt/e
PART 1. The first part of the lecture is organized around the following topics:
Chapter 1. Basics of optimization
Chapter 2. Simulation algorithms for optimization
Chapter 3. Dynamic neural networks based simulation of Shortest Path Problems (SPP)
Chapter 4. Dynamic neural networks based simulation of Traveling Salesman Problems (TSP)
Literatur
Textbooks
[1] Martin Treiber, and Arne Kesting, „Traffic Flow Dynamics: Data, Models and Simulation,“ Springer-Verlag, Berlin Heidelberg, ISBN 978-3-642-32460-4, 2013
[2]. F. M. Ham and I. Kostanic, „Principles of Neurocomputing for Science , & Engineering,“ New York, NY, USA: McGraw-Hill, 2001.
[3] Adam B. Levy, „The Basics of Practical Optimization,“ SIAM, The society of industrial and applied mathematics, ISBN 978-0-898716-79-5, 2009
[4] Nocedal J. and Wright S.J., „Numerical Optimization,“ Springer Series in Operations Research, Springer, 636 pp, 1999.
[5] Saidur Rahman, „Basics of Graph Theory,“ Springer, ISBN: 978-3-319-49474-6, 2017
Journal Papers
[1] J. C. Platt and A. H. Barr, “Constrained differential optimization for neural networks,” American Institute of Physics, Tech. Rep. TR- 88-17, pp. 612-621, Apr. 1988.
[2] I. G. Tsoulos, D. Gavrilis, and E. Glavas, “Solving differential equations with constructed neural networks,” Neurocomputing, vol. 72, nos. 10–12, pp. 2385–2391, Jun. 2009.
[3] J.C. Chedjou, and K. Kyamakya, "A universal concept for robust solving of shortest path problems in dynamically reconfigurable graphs," Mathematical Problems in Engineering, 2015.
[4] J.C. Chedjou, and K. Kyamakya, "Benchmarking a recurrent neural network based efficient shortest path problem (SPP) solver concept under difficult dynamic parameter settings conditions," Neurocomputing, Elsevier, pp. 175-209, Vol. 196, 2016.
[5] J.C. Chedjou, K. Kyamakya, and N. A. Akwir "An efficient, scalable, and robust neuro-processor-based concept for solving single-cycle traveling salesman problems in complex and dynamically reconfigurable graph networks," IEEE Access, pp. 42297-42324, Vol. 8, 2020.
Prüfungsinformationen
Beurteilungskriterien/-maßstäbe
PART 1. This part corresponds to 50% of the overall grade of the lecture. The final grade (for PART 1) is obtained as follows:
1. Participation in the lecture and answering questions correspond to the oral examination. This is assessed with 12.5% of the final grade of the lecture.
2. The homework correspond to 12.5% of the overall grade of the lecture.
3. Final projects (to be defined by the lecturer) correspond to 25% of the final grade of the lecture.
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
- 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Fach: Technical Complements (NC, ASR)
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
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Sommersemester 2023
- 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Wintersemester 2022/23
- 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Sommersemester 2021
- 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Sommersemester 2020
- 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Wintersemester 2018/19
- 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Wintersemester 2017/18
- 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Wintersemester 2016/17
- 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Wintersemester 2015/16
- 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
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Wintersemester 2014/15
- 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
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Wintersemester 2013/14
- 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
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Wintersemester 2012/13
- 700.372 KU Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)