700.372 (19W) Optimisation and Neural Network based Simulation Lab for Transportation and Logistics

Wintersemester 2019/20

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LV-Titel englisch Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
LV-Art Kurs (prüfungsimmanente LV )
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 3.0
Anmeldungen 0 (30 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 01.10.2019

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LV-Beschreibung

Intendierte Lernergebnisse

This lecture familiarizes students with the development of simulation algorithms (using MATLAB and SIMULINK) for the analysis of systems, scenarios and phenomena in transportation. Various MATLAB codes are developed to solve linear and nonlinear optimization problems. MATLAB codes are also developed for solving the shortest path problem (SPP) and the  traveling salesman problem (TSP) in graph networks. SIMULINK is further used to implement the „Car-following“ model and the analysis of the dynamics of „HEADWAY“ on  arterial roads. 

Students are also familiarized (through this lecture) with the simulation of the dynamics of traffic flow modeled by nonlinear partial differential equations.

Overall, the main objectives of this lecture are expressed by the following keywords:  Simulation algorithms (based on Neural Networks) for the solving of linear and nonlinear optimization problems; Simulation algorithms (based on Neural Networks) for the solving of shortest path problems (SPP);  Simulation algorithms (based on Neural Networks) for the solving of  traveling salesman problems (TSP); Design of the SIMULINK scheme for the analysis of „Car following“ model; Simulation algorithms based on the fourth-order Runge - Kutta  method for the simulation of the traffic flow dynamics modeled by coupled nonlinear Partial Differential Equations.

The general expectation regarding the knowledge to be provided/acquired is as follows:

  • 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 in graph networks.
  • Mastering of the development of simulation algorithms (based on Recurrent Neural Networks) for the solving of traveling salesman problems in graph networks.
  • Mastering of the design and implementation of the SIMULINK scheme for solving the „Car-following“ model in transportation and the analysis of the dynamics of „HEADWAY“ on arterial roads.
  • Mastering of the development of simulation algorithms for the solving of both macroscopic traffic flow (PDEs) and microscopic traffic flow (ODEs) models.

Lehrmethodik inkl. Einsatz von eLearning-Tools

1. The Lecturer provides full explanation of how to write numerical codes to solve the exercises proposed in each chapter of the content of the Lecture provided below.

The following MATLAB codes will be explained by the Lecturer:

* Numerical codes for linear optimization: The linear programming (LP) toolbox in MATLAB.

* Numerical codes for nonlinear optimization: The quadratic programming (QP) toolbox in MATLAB.

*  MATLAB codes/algorithms for solving the shortest path problem (SPP) using the  Basic Differential Multiplier Method (BDMM)- Neurocomputing.

*  MATLAB codes/algorithms for solving the traveling salesman problem (TSP) using the  Basic Differential Multiplier Method (BDMM)-Neurocomputing.

* SIMULINK scheme for the numerical simulation of the „Car-following“ model.

* MATLAB codes/algorithms based on the Runge-Kutta method for the analysis of the traffic dynamics of arterial roads modeled by nonlinear partial differential equations.

2. Students in groups of two must develop/write numerical codes to solve the exercises proposed by the Lecturer as additional application examples for the good understanding of the lecture.

3. Numerical codes are developed by students (in groups of two) as projects. These codes are developed in accordance to each of the chapters defined below (see content of the Lecture).

Inhalt/e

Chapter 1. Introduction to traffic simulation, management and control.

Chapter 2. Overview on traffic simulation models: Macroscopic and microscopic models.

Chapter 3. Simulation algorithms for optimization: Linear programming (LP) and Quadratic programming (QP) for linear and nonlinear optimization with all types of constraints

Chapter 4. Simulation algorithms for optimization: The Basic Differential Multiplier Method (BDMM)/Neurocomputing) for linear and nonlinear optimization with all types of constraints

Chapter 5.  Modeling of the Shortest Path Problem (SPP) in graph networks and simulation algorithms (based on Recurrent Neural Networks) for  the solving of Shortest Path Problems. 

Chapter 6.  Modeling of the Traveling Salesman Problem (TSP) in graph networks and simulation algorithms (based on Recurrent Neural Networks) for  the solving of Traveling Salesman Problems. 

Chapter 7. Simulation of traffic flow on arterial roads: Design of the „Car-following“ model in SIMULINK and analysis of the „HEADWAY“ dynamics on arterial roads.

Chapter 8.  Simulation algorithms (based on the 4th order Runge-Kutta method) for the analysis of the dynamics of traffic flow on arterial roads modeled by nonlinear Partial Differential Equations (PDE).

Chapter 9.  Mathematical modeling of Supply Chain Networks (SCN) and simulation algorithms for the analysis of the dynamics of SCN modeled by coupled models: Case 1. Continuous models and Case 2. Discrete models.


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.

Prüfungsinformationen

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.

Prüfungsmethode/n

1.  Type of assessment of the course: Written exam at the end of the lecture 

2. Duration: 3 to 4 hours

Prüfungsinhalt/e

* All chapters of the lecture

(The final exam takes into account all chapters of the lecture.)

Beurteilungskriterien/-maßstäbe

The following three possibilities/options are offered as evaluation criteria:

Option 1. * Exam without BONUS (100 /%).

 

Option 2. * Exam (100 /%) + BONUS 1.

•* BONUS 1. Participation in the course (i.e., answers to questions) (25% of the total exam).

Note:the answer to questions is not mandatory.

 

Option 3. * Exam (100 /%) + BONUS 2.

•* BONUS 2. homework (25% the total of the exam).

Note:The homework is not compulsory.

Beurteilungsschema

Note Benotungsschema

Position 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)
  • 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)
  • 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)
  • 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)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Autonomous Systems and Robotics: Advanced (ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technischer Schwerpunkt (Intelligent Transportation Systems) (Pflichtfach)
      • 1.4-1.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)
  • Masterstudium Information Technology (SKZ: 489, Version: 06W.3)
    • Fach: Technische Ergänzung II (Pflichtfach)
      • 3.4-3.5 Kurs oder Labor ( 4.0h KU / 6.0 ECTS)
        • 700.372 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics (2.0h KS / 3.0 ECTS)

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Sommersemester 2022
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Sommersemester 2020
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Wintersemester 2018/19
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Wintersemester 2017/18
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Wintersemester 2016/17
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Wintersemester 2015/16
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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)