Number of hours
- Lectures 24.0
- Projects -
- Tutorials 24.0
- Internship -
- Laboratory works -
- Written tests -
ECTS
ECTS 6.0
Goal(s)
(This class is conducted in English)
Please review the required skills prior to enrollment. While programming skills are not mandatory, they are recommended.
In this course, you will learn how to apply Artificial Intelligence techniques, especially Machine Learning, to support and enhance decision-aiding processes. By the end of the course, you will be able to understand and develop models that use AI to inform strategic decisions in complex, multi-criteria environments.
Responsible(s)
Content(s)
This course focuses on Artificial Intelligence (AI), specifically machine learning, within the context of decision aiding. Emphasis is placed on utilizing machine learning as a decision aiding tool. The content comprises:
• Introduction to machine learning techniques for decision support problems, including supervised/unsupervised and reinforcement methods. Programming is not discussed in details.
• Case studies employing these techniques, accompanied by a literature review for group work.
• Involvement of industrial partners, providing insights from their experiences.
• General knowledge of manufacturing and supply chain management, including scheduling and inventory management.
• Ability to conduct a literature review, including searching and analyzing scientific articles.
• Basic understanding of statistics, including correlation analysis and linear regression, is appreciated.
• Familiarity with Python for utilizing certain packages is beneficial but not mandatory.
• Knowledge of simulation software (e.g., Arena) is helpful but not required.
Test
Evaluation: The primary assessment is based on a group report for each case study, supplemented by group presentations and individual work. ->("A1")
Evaluation of session 1 (N1) = "A1" (with penalties applied for any unjustified absences)
Evaluation of session 2 (N2) = Synthesis of articles and interview ("A2")
Repeated and unjustified absences will impact the final score and may lead to a score up to 0 for this class.
Using generative AI for writing reports is prohibited and lead you to a score of 0.
The exam is given in english only 
Calendar
The course exists in the following branches:
- Curriculum - Engineer IPID apprentice program - Semester 9 (this course is given in english only
) - Curriculum - Engineer student Master SCM - Semester 9 (this course is given in english only )
- Curriculum - Master 2 GI SIE program major SPD - Semester 9 (this course is given in english only )
- Curriculum - Engineer student Master PD - Semester 9 (this course is given in english only )
- Curriculum - Master 2 GI GID major GOD - Semester 9 (this course is given in english only )
- Curriculum - Master 2 GI GID major DPD - Semester 9 (this course is given in english only )
- Curriculum - Master 2 GI SIE program major SOM - Semester 9 (this course is given in english only )
Additional Information
Course ID : 5GUC4204
Course language(s):
You can find this course among all other courses.
Bibliography
• PM Seeger, Z Yahouni, G Alpan, (2021). Literature review on using data mining in production planning and scheduling within the context of cyber physical systems. Journal of Industrial Information Integration 28, 100371
• Cadavid, J. P. U., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing , 1-28
• Faustmann, G. (2019). Application of machine learning in production scheduling (Doctoral dissertation, Wien).
•Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems . O'Reilly Media.