Duration
30h Th
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The aim of this course is to present various aspects of mathematical modeling and of problem-solving strategies as they are used in operations research for the solution of realistic, large-scale, complex problems.
The course contains several independent parts:
- General-purpose heuristic strategies for the solution of combinatorial optimization problems, such as simulated annealing, tabu search or genetic algorithms; the practical implementation of such methods is illustrated on a variety of optimization problems.
- Integer programming and network problems. Branch-and-bound method. Modeling and solution of large-scale models.
Learning outcomes of the learning unit
By the end of this course, the students will be able to model complex decision-making problems and to implement appropriate methods for their solution. They will better understand the opportunities offered by optimization methods, as well as their intrinsic limitations.
As a side-benefit, they will also develop advanced computer programming skills that are transferable to different business contexts.
Intended Learning Outcomes addressed by the course:
- Establish a strategy in order to optimize the value chain of an organization, taking into account its scientific and technological context, and demonstrating a critical mind and scientific precision.
- Take charge of the everyday management of a company, an organization or a project, taking into account its scientific, technological and entrepreneurial dimensions, ensuring a good interface between its technological and managerial aspects, and capitalizing on the characteristics of a digitalized world.
- Plan and implement the performance and quality control in a company, an organization or a project, using the appropriate analytical tools.
- Communicate efficiently in English, internally and externally, about a company, organization or project.
- Adapt his/her managerial practice to the needs of a fast-evolving world, showing curiosity, scientific precision, creativity, and autonomy.
Prerequisite knowledge and skills
Prerequisites:
- Mathematics: calculus and matrix algebra.
- Operations research: an introductory course covering linear programming models and methods.
- General proficiency with personal computers.
Command of a computer programming language (MathLab, SciLab, Julia, Python, C, Visual Basic,...) is an asset, but is not a strict prerequisite. An introduction to a specific language (e.g., Julia) will be provided, and the students are encouraged to use it for the development of their projects.
Planned learning activities and teaching methods
Group and individual projects: computer implementations, written reports and oral presentations.
Mode of delivery (face-to-face ; distance-learning)
Lectures and computer labs. Group and individual projects: computer implementations, reports and presentations. Attendance is mandatory.
Recommended or required readings
Lecture notes: Y. Crama, Computational Optimization, ULg, 2016.
Assessment methods and criteria
The final note will be based on:
- the evaluation of group projects (written reports and oral presentations): 75% of the final note
- individual assessment (homework and oral exam in May-June): 25%
Work placement(s)
Organizational remarks
This course is taught in English.
See the Lola Web site
http://lola.hec.ulg.ac.be/index.php
for additional information.
Contacts
Instructor: Prof. Y. Crama
HEC Liège (Building N1)
Teaching assistant: Marie Baratto
HEC Liège (Building N1) Room 334
Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session
Teaching methods implemented : distance-learning
Two lectures have been replaced by distance-learning activities:
- March 19, lecture 10, Integer programming and branch-and-bound: recorded presentation and slides.
- March 26, lecture 11, Integer programming - Julia Lab: exercises with distant support provided by the instructor and a teaching assistant, on demand.
Assessment subjects
The contents of the course have been almost completely presented in class. The last two lectures (distant learning sessions) have covered an additional topic. The expected learning outcomes are the same as initially planned in the syllabus.
Assessment methods
The final assessment will be based on
- homeworks,
- written reports on the projects "Triangulation of input-output matrices" and "Course timetabling",
- peer evaluation of the work produced by members of each group, and individual participation.
In addition, a short oral examination will take place in order to assess the individual mastery of the concepts implemented in the projects (first project only, through videoconferencing in May-June; if possible, both projects face-to-face in August-September).
See the Lola website http://lola.hec.uliege.be/course/view.php?id=348 for additional information.
Contacts
Instructor: Prof. Y. Crama
Yves.Crama@uliege.be
Teaching assistant: Marie Baratto
Marie.Baratto@uliege.be