Operations Research for Logistics - 4GUL0209

Goal(s)

The course is taught in French (some e-learning activities are in English).

Acquire skills in integer linear programming, dynamic programming and graph theory to model and solve complex logistics problems.
Understanding the role of Operations Research in our industrial society.

Four expectations:

1- Knowing the tools: Knowing the objects handled (mastering the terminology of linear programming, dynamic programming and graphs, knowing how to use the right vocabulary in describing a problem and solving it);

2- Know how to apply the tools: know and know how to carry out simple reasoning (provide proofs for linear programming and graphs, know how to propose recurrence formulas for dynamic programming), and know and know how to carry out basic graph algorithms (basic linear programming algorithms are expected to be mastered, see Prerequisites);

3- Identify the right tools to solve concrete problems: Model concrete problems using the tools seen in class, and deduce the algorithm(s) needed to solve the problem. Take a step back to reflect on modeling choices and their associated societal and environmental impacts.

4- Know how to question the relevance and impacts of using tools when addressing a real-world problem.

Responsible(s)

Bernard PENZ

Content(s)

The course follows the principle of reverse pedagogy. It therefore requires a high degree of autonomy and personal organization. Working together in a small team (3 to 5 students) enables students to exchange and share their knowledge, which in turn leads to better progress and more solid learning.

The course is organized around :

• Learning sequences (validation of expectations 1 and 2) :
  - Independent reading (CM for PLNE);
  - Application and modeling exercises to be prepared and Caseine activities to be carried out before the TD;
  - A review with the TD teacher, who will answer any questions and clarify any points that have caused difficulties;

• Case studies (in groups of 3 to 4 students) (validation of expectations 1, 2 and 3):
  - Model the problem posed;
  - Solve the problem using the tools provided (Python, PL solver);
  - Write a synthesis;

• A work on the map of the limits of industrial thinking (in groups of 3 to 4 students):
  - Explore the map by following lines of inquiry;
  - Read texts and provide key ideas.

Contents

Content of the eight learning sequences :

• Session 1: Graph theory: basic concepts;
• Seq. 2a: Chain, cycle, path, circuit;
• Seq. 2b: Minimum-weight spanning trees;
• Seq. 3: Shortest path;
• Session 4: Dynamic programming;
• Seq. 5: Maximum flow and minimum cost flow;
• Seq. 6: Integer linear programming;
• Seq. 7: Modeling concrete problems.

Content of the case studies :

• Case Study 1: Dynamic Programming - modeling and solving a problem (Python programming language);
• Case Study 2: Integer Linear Programming - modeling and solving a problem (OPL - Cplex).

Content of the study on the map of the limits of industrial thinking:

• Lecture presenting the lines of inquiry on the map;
• Reading workshops in tutorial format.

The course requires knowledge of :
- Linear programming: Linear Programming modeling, simplex algorithm, duality, economic interpretation (see description of UE Mathématiques et informatique décisionnelles - 3GUC0905)
- Algorithms and programming: basic notions of algorithms and mastery of a high-level programming language (e.g. Python) (see description of UE Modélisation mathématique et informatique

• 3GUC0205 and UE Mathématiques et informatique décisionnelles - 3GUC0905).

Test

Session 1
The UE is assessed following a 45-minute individual interview.

Resit session
The UE is assessed following a 1 hour and 30-minute individual interview.

The jury may decide to allow progression to the next year subject to the deferred validation of this course unit. This decision remains exceptional; the jury has full discretion for each student.

Le calcul de la note finale est compatible avec une organisation des enseignements et des examens en distanciel.

La note est proposé par l'enseignant·e à l'issue de l'entretien individuel de fin de semestre.

Calendar

The course exists in the following branches:

• Curriculum - Engineer student Master SCM - Semester 7

Additional Information

Course ID : 4GUL0209
Course language(s):

You can find this course among all other courses.

Bibliography

C. Guéret, C. Prins, M. Sevaux, Programmation lineaire: 65 problèmes modélisés et résolus avec l'outil Visual Xpress, Eyrolles, 2000.
V. Giard, Gestion de la production et des flux, Economica, 2003.
H. Stadtler, C. Kilger, Supply Chain Management and Advanced Planning, Springer, 2002.
Y. Pochet, L.A. Wolsey, Production Planning by Mixed Integer Programming, Springer, 2006.
M. Minoux, M. Gondran, Graphes et Algorithmes, Lavoisier, 2009.