700.303 (19W) Mathematical Modeling Methods for Transportation and Logistics

Wintersemester 2019/20

Anmeldefrist abgelaufen.

Erster Termin der LV
01.10.2019 12:00 - 14:00 , B04.1.02 ICT-Labor
Nächster Termin:
22.10.2019 12:00 - 14:00 , B04.1.02 ICT-Labor

Überblick

Lehrende/r
LV-Titel englisch
Mathematical Modeling Methods for Transportation and Logistics
LV-Art
Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
3.0
Anmeldungen
0 (5 max.)
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
01.10.2019

LV-Beschreibung

Intendierte Lernergebnisse

This lecture give students technical knowledge of traffic, transportation and logistics. A particular attention will be devoted to the modeling processes in transportation and logistics. Models will be obtained/derived to explain and control/optimize the dynamics of systems, scenarios, and phenomena in transportation. Theoretical concepts/methods will be developed to model/represent given systems, scenarios, and phenomena in transportation. The developed models will be expressed in graphical and/or mathematical forms.

Overall, the main objectives of this lecture are expressed by the following keywords: Methods and models in transportation; Traffic and transport; Supply chains and logistics; Optimization with application to Real-life transportation scenarios/prenomena.

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

  • Understanding of basic systems, scenarios and phenomena in transportation.
  • Mastering of the basics of optimization.
  • Mastering the application of optimization in transportation
  • Mastering of the modeling of shortest path problems with applications in transportation.
  • Mastering of the modeling of traveling salesman problems with applications in transportation.
  • Mastering of the mathematical modeling of traffic flow at macroscopic level using Partial differential equations (PDEs); Applications in practice for the modeling of real traffic scenarios on arterial roads.
  • Mastering of the mathematical modeling of traffic flow at microscopic level using Ordinary Differential Equations (ODEs); Applications in practice for the modeling of real traffic scenarios on arterial roads.
  • Understanding of the functioning principle of supply chain networks (SCN) and their modeling principle.
  • Acquiring some basic knowledge in logistics and scheduling.

Lehrmethodik

  • The slides are available for the whole lecture. These slides are uploaded in the MOODLE system. The full content of each slide is systematically explained by the Lecturer. Additional examples which are not included in slides will be proposed by the Lecturer to allow good understanding of the information provided.  
  • The slides contain exercices with solutions for the good understanding of the content of each chapter. These solutions are systematically explained (during the lecture) by the Lecturer.
  • The Slides contain exercices without solutions to be solved by students during the lecture (this is part of oral exam). The students are fully assisted by the Lecturer in order to obtain correct/exact solutions to the proposed exercices. This will help to check whether the students have understood the chapters or not.
  •  Several exercices will be proposed by the Lecturer to be solved by students as projects. This will help to test the self-learning potential of students.

Inhalt/e

Chapter 1. General introduction: Definition of some keywords (e.g. Method, Transportation, Informatics, Logistics, System, Dynamic Systems, System Theory, Modeling, Model, System identification, Simulation, etc.); Principles of modeling Dynamic Systems; Examples of systems' models in engineering.

Chapter 2. Basics of graph theory: Examples of communication schemes (V2V, V2I, Road traffic scenarios, self-organized road traffic control, etc.); Overview on graph theory; Shortest path (SP); Shortest Path spanning tree (SPST); Minimum spanning tree (MST); Traveling Salesman Problem (TSP); Applications of SP, SPST, MST, TSP in transportation; Graphs and Matrix representations.

Chapter 3. Basics of optimization: Optimization and key parameters; Methods in Operations Research; Classical optimization methods: Linear programming (LP); Quadratic programming (QP); Integer programming; Real-valued programming; Stochastic programming; Hessian Matrix as a test condition; Lagrangian method; Method of constrained optimization: Penalty method; Lagrange multiplier method; Gradient methods.

Chapter 4. Statistical analysis of stochastic phenomena: Deterministic vs. Stochastic formalisms; Fundamental parameters of a stochastic process and measurements; Normal vs. Standard normal distributions (Z-score); Level of confidence; Factors affecting the confidence interval range; The Z-score table; Application examples in transportation.

Chapter 5. Basics of traffic theory: Overview of traffic processes; Probability distributions; Overview of queuing (main phases of a queuing process , elements of a queuing system, and some applications of queuing); General queuing notation (Kendall 1951); Queuing models; State analysis of queue models/systems; Mathematical modeling of a single-server queuing system).  

Chapter 6. Mathematical modeling of traffic flow on arterial roads: Key parameters of traffic flow: Flow, speed, and density; Greenshields model; Calibration of Greenshields Model; Shock waves; Rarefaction waves; Macroscopic traffic flow models expressed by PDEs. Microscopic traffic flow models expressed by ODEs; Presentation of concrete traffic flow scenarios with corresponding mathematical models.

Chapter 7. Basics of traffic signals control at isolated junction: Performance criteria of a junction and mathematical models; Identification of a traffic junction; Classification of traffic into streams; Phase- groups; Traffic signal phasing and timing plan; Protected- and Unprotected- turns; Critical lane concept; Cycle length; Green time; All-red interval; Delays; Dilemma zones; Pedestrian crossing time; Level of service (LOS); Some illustrative examples from practice.

Chapter 8. Basics of supply chain networks (SCN) and modeling principles: Supply chain management (SCM): Integration and management of business processes; Structure of a SCN; Framework for SCM; Different types of intercompany business process links; Different types of intercompany business process links brackdown; Fundamental management components in a supply chain network; General design principle of a SCN; Graphical modeling of a SCN; Mathematical modelling of a SCN.

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

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 (ie 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/Grade 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)
        • 700.303 Mathematical Modeling Methods 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)
        • 700.303 Mathematical Modeling Methods 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)
        • 700.303 Mathematical Modeling Methods 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)
        • 700.303 Mathematical Modeling Methods 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)
        • 700.303 Mathematical Modeling Methods 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)
        • 700.303 Mathematical Modeling Methods 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)
        • 700.303 Mathematical Modeling Methods for Transportation and Logistics (2.0h KS / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Wintersemester 2018/19
  • 700.303 KS Mathematical Modeling Methods for Transportation and Logistics (2.0h / 3.0ECTS)
Wintersemester 2017/18
  • 700.303 KS Methods of Transportation Informatics and Logistics (2.0h / 3.0ECTS)
Wintersemester 2016/17
  • 700.303 KS Methods of Transportation Informatics and Logistics (2.0h / 3.0ECTS)
Wintersemester 2015/16
  • 700.303 KS Methods of Transportation Informatics and Logistics (2.0h / 3.0ECTS)
Wintersemester 2014/15
  • 700.303 KU Methods of Transportation Informatics and Logistics (2.0h / 3.0ECTS)
Wintersemester 2013/14
  • 700.303 KU Methods of Transportation Informatics and Logistics (2.0h / 3.0ECTS)
Wintersemester 2012/13
  • 700.303 KU Methods of Transportation Informatics and Logistics (2.0h / 3.0ECTS)