UE AI & Machine Learning for Decision Aiding - 5GUC4204

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 basic models that use AI to inform strategic decisions in complex, multi-criteria environments.

Responsible(s)

Zakarya YAHOUNI

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.

Test

Evaluation: The primary assessment is based on a group and individual work. Evaluation will be based on reports and/or presentation for each case study, supplemented by individual work consisting of written exam that can be a quiz (A1).

Evaluation of session 1 (N1) = "A1" (with penalties applied for any unjustified absences)
Evaluation of session 2 (N2) = Synthesis of articles suplemented by interview with professor and written exam than can be a quiz ("A2")

Repeated unjustified absences or late arrivals will impact the final A1 score and may result in a score of 0.

Using generative AI for writing reports and analysis is prohibited and lead you to a score of 0. Using generative AI to generate python code during the project is allowed, however you need to specify the tools and the way you use it.

You are responsible for distributing tasks fairly within your group. If a student is reported by their group for not contributing, they will receive a score of 0 for the concerned project.

The exam is given in english only

Calendar

The course exists in the following branches:

• Curriculum - Engineer student Master SCM - Semester 9 (this course is given in english only )
• Curriculum - Engineer student Master PD - Semester 9 (this course is given in english only )
• Curriculum - Engineer IPID apprentice program - Semester 9 (this course is given in english only )
• Curriculum - Master 2 GI SIE - Semester 9 (this course is given in english only )
• Curriculum - Master 2 GI GID - 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

• Hosseini, A., Yahouni, Z., Feizabadi, M. (2023). Scheduling AIV transporter using simulation-based supervised learning: A case study on a dynamic job-shop with three workstations, IFAC-PapersOnLine 56 (2), 8591-8597

• 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.