700.372 (18W) Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
Überblick
 Lehrende/r
 LVTitel englisch
 Optimisation and Neural Network based Simulation Lab for Transportation and Logistics
 LVArt
 Kurs (prüfungsimmanente LV )
 Semesterstunde/n
 2.0
 ECTSAnrechungspunkte
 3.0
 Anmeldungen
 4 (30 max.)
 Organisationseinheit
 Unterrichtssprache
 Englisch
 LVBeginn
 02.10.2018
 eLearning
 zum MoodleKurs
Zeit und Ort
LVBeschreibung
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 „Carfollowing“ 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 fourthorder 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 „Carfollowing“ 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
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 „Carfollowing“ model.
* MATLAB codes/algorithms based on the RungeKutta 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 „Carfollowing“ model in SIMULINK and analysis of the „HEADWAY“ dynamics on arterial roads.
Chapter 8. Simulation algorithms (based on the 4th order RungeKutta 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,“ SpringerVerlag, Berlin Heidelberg, ISBN 9783642324604, 2013
[2]. F. M. Ham and I. Kostanic, „Principles of Neurocomputing for Science , & Engineering,“ New York, NY, USA: McGrawHill, 2001.
[3] Adam B. Levy, „The Basics of Practical Optimization,“ SIAM, The society of industrial and applied mathematics, ISBN 9780898716795, 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: 9783319494746, 2017
Journal Papers
[1] J. C. Platt and A. H. Barr, “Constrained differential optimization for neural networks,” American Institute of Physics, Tech. Rep. TR 8817, pp. 612621, 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
Prüfungsmethode/n
1. Written exam: Will be held at the end of the Lecture (50%)
2. Oral exam: Will be held during the lecture (it is about the involvement of the students in all chapters of the lecture) (30%)
3. Homework: This measures the potential of students with regard to selflearning or learning/working autonomously (20%)
Prüfungsinhalt/e
1. Development of MATLAB codes for the optimization of *Linear functions and *Nonlinear functions using Linear programming (LP) and Quadratic programming (QP) toolboxes. The case of all types of constraints will be considered.
2. Development of MATLAB codes based on the Basic Differential Multiplier Method (BDMM) / Neurocomputing for the optimization of *Linear functions and *Nonlinear functions . The case of all types of constraints will be considered. Comparison of the results obtained using the LP and QP toolboxes in MATLAB with the results obtained using Neurocomputing.
3. Development of MATLAB codes based on the Basic Differential Multiplier Method (BDMM) / Neurocomputing for the solving of Shortest Path Problems (SPP): Case 1. Directed graph networks and Case 2. Undirected graph networks.
4. Development of MATLAB codes based on the Basic Differential Multiplier Method (BDMM) / Neurocomputing for the solving of Traveling Salesman Problems (TSP): Case 1. Directed graph networks and Case 2. Undirected graph networks.
5. Design and Synthesis of a SIMULINK scheme for the solving of the „Carfollowing“ model. Analysis of the interaction between cars/vehicles on arterial roads and monitoring (i.e. control and optimization) of the „HEADWAY“ dynamics .
6. Development of MATLAB codes for the analysis of the dynamical behavior of traffic flow (at macroscopic level of details) modeled by coupled nonlinear Partial Differential Equations (PDEs).
7. Development of MATLAB codes for the analysis of the dynamical behavior of Supply Chain Networks: Case 1. Continuous models. Case 2. Discrete models.
Beurteilungsschema
Note/Grade 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 LVKatalog (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 LVKatalog (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 LVKatalog (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 LVKatalog (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 LVKatalog (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 LVKatalog (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 LVKatalog (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 LVKatalog (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: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)

Wahl aus dem LVKatalog (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)

Wahl aus dem LVKatalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)

Fach: Autonomous Systems and Robotics: Advanced (ASR)
(Wahlfach)
 Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)

Fach: Technischer Schwerpunkt (Intelligent Transportation Systems)
(Pflichtfach)

1.41.5 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)

1.41.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)

Fach: Technischer Schwerpunkt (Intelligent Transportation Systems)
(Pflichtfach)
 Masterstudium Information Technology
(SKZ: 489, Version: 06W.3)

Fach: Technische Ergänzung II
(Pflichtfach)

3.43.5 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)

3.43.5 Kurs oder Labor (
4.0h KU / 6.0 ECTS)

Fach: Technische Ergänzung II
(Pflichtfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
 Sommersemester 2020

 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)