• Ries Bernard

• Tellache Nour Elhouda

How can tasks be efficiently scheduled in factories, projects managed under tight deadlines, or timetables constructed in complex systems such as schools or hospitals? This course will introduce the field of machine scheduling, linking theoretical foundations with practical applications. The course will begin with a review of essential concepts in computational complexity, graphs, and algorithms, followed by the definitions and classification of machine scheduling environments (single-machine, parallel-machine, and dedicated-machine systems). For each environment, the course will examine computational complexity, highlighting the boundary between polynomially solvable and NP-hard cases. Important polynomial cases will be presented together with established efficient algorithms, while NP-hard problems will be addressed through benchmark exact and approximation methods. Applications in manufacturing, project planning, and timetabling will be integrated throughout to demonstrate the practical relevance of scheduling theory.

Students will gain the ability to understand theories of machine scheduling, analyze problem complexity, and apply exact and approximation algorithms to design efficient schedules for real-world applications.

knolowdge on algorithmic preferably following courses Decision Support I and/or II.

Błażewicz, J., Ecker, K.H., Pesch, E., Schmidt, G. and Węglarz, J., 2007. Handbook on scheduling: from theory to applications. Berlin, Heidelberg: Springer Berlin Heidelberg.

Pinedo, M. and Hadavi, K., 1992. Scheduling: theory, algorithms and systems development. In Operations research proceedings 1991: Papers of the 20th annual meeting/vorträge der 20. Jahrestagung (pp. 35-42). Berlin, Heidelberg: Springer Berlin Heidelberg.

Main exam and catch up exam : Written Exam - 90 minutes