CIVIL-557 / 4 credits

Teacher(s): Varotto Silvia Francesca, Torres Duran Fabian Alejandro

Language: English

Remark: The course is given by various lecturers


Summary

The course proposes an introduction to operations research, big data analysis, and mathematical modelling for decision support in transportation systems.

Content

Learning Prerequisites

Required courses

Recherche opérationnelle

Learning Outcomes

By the end of the course, the student must be able to:

  • Model decision processes in transportation systems as optimization problems
  • Implement and solve optimization problems using state-of-the-art solvers, i.e., CPLEX.
  • Choose an appropriate optimization approach.
  • Analyze and model big data using state-of-the-art mathematical methods.
  • Choose an appropriate data analysis and modelling approach.

Teaching methods

The optimization, data analysis and modelling approaches will be presented and applied to real world case studies during lectures. The students will apply the methods learnt in class during the laboratory sessions and work in groups on a project with real data.

Assessment methods

  • At the end of each module, each group will submit a project report.
  • At the end of the course, each group will present the project during an oral exam. The assessment will be based on the quality of the report, the quality of the presentation and the answers to the questions. The oral exam will account for 80% of the final grade.
  • At the end of the course, each student will complete a written exam. The written exam will include multiple choice and short answer questions, and it will account for 20% of the final grade.

In the programs

  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Decision-aid methodologies in transportation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Decision-aid methodologies in transportation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Decision-aid methodologies in transportation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Decision-aid methodologies in transportation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Decision-aid methodologies in transportation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22