Let students earn a comprehensive knowledge of mathematical models for machine scheduling along with their relationship with project scheduling models. Students learn how to classify these problems in terms of computational complexity and solve them by heuristic, approximation or exact algorithms. This topics are presented so as to stress their relationship with the fundamental Combinatorial Optimization problems and the techniques of Integer Programming. Although the course is marked on strong a methodological basis, it provides pointers to all the steps of the lifecycle of industrial applications, from recognizing scheduling problems in industrial contexts to developing and experimenting algorithms. Students are trained to these aspects by implementation projects and case studies.
Elements of a (deterministic) scheduling problem, examples of practical applications. Classification of scheduling problems. Integer Linear Programming formulations. Single machine scheduling: computational complexity, heuristic and exact algorithms. Parallel machine scheduling: exact, heuristic and approximation algorithms. Relationships with basic Combinatorial Optimization problems. Optimization problems in Project Scheduling. Job Shop scheduling: formulations, heuristic and exact algorithms.