700.372 (22W) Optimisation and Neural Network based Simulation Lab for Transportation and Logistics

Wintersemester 2022/23

No registration period specified.

First course session
12.10.2022 14:00 - 16:00 Online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
Lecturer
Course title german Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
Type Course (continuous assessment course )
Course model Blended learning course
Online proportion 30%
Hours per Week 2.0
ECTS credits 3.0
Registrations 6 (30 max.)
Organisational unit
Language of instruction Englisch
possible language(s) of the assessment English
Course begins on 12.10.2022
Seniorstudium Liberale Yes

Time and place

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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Course Information

Intended learning outcomes

This lecture 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.
  • Mastering of the computation based Neural Network: Application for solving concrete case studies of practical interest in engineering.

Teaching methodology including the use of 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.

Course content


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)

Chapter 5. Introduction to Neural Networks

Chapter 6. Application of Neural Networks for solving selected concrete application examples in engineering

Literature

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.

Examination information

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • 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)
  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Technical Complements (NC, ASR) (Compulsory elective)
      • 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)
  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • 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)
  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Technical Complements (NC, ASR) (Compulsory elective)
      • 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)
  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Subject: Information and Communicatons Enginnering: Supplements (Compulsory elective)
      • 1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) ( 0.0h VC, KS / 14.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)

Equivalent courses for counting the examination attempts

Sommersemester 2023
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2022
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2021
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Sommersemester 2020
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2018/19
  • 700.372 KS Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2017/18
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2016/17
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2015/16
  • 700.372 KS Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2014/15
  • 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
Wintersemester 2013/14
  • 700.372 KU Simulation Lab for Transportation and Logistics (1.0h / 1.5ECTS)
Wintersemester 2012/13
  • 700.372 KU Simulation Lab for Transportation and Logistics (2.0h / 3.0ECTS)