The objective of the course is to endow the students with advanced knowledge for operations planning and scheduling in manufacturing and logistics systems. Topics include deterministic operations research methodology for the design of decision support systems, modeling, algorithms and applications.
Curriculum
teacher profile teaching materials
Gradient, Hessian
Local minimum, Necessary conditions (first and second order)
Local minimum, Sufficient conditions (secondo order and convex case)
Gradient method, Line search
Newton method,
2. Constrained non-linear programming
KKT conditions
Barrier method and Penalty functions
3. Lot Sizing
EOQ model
Wagner-Whitin Algorithm
Zangwill Algorithm
4. Job Shop Scheduling
Exact methods, Carlier-Pinson Algorithm
Euristhic methods, Nowicki-Smutnicki Algorithm
5. Vehicle Routing Problem
6. Crew Scheduling
7. Plant Location
Programme
1. Non-linear programmingGradient, Hessian
Local minimum, Necessary conditions (first and second order)
Local minimum, Sufficient conditions (secondo order and convex case)
Gradient method, Line search
Newton method,
2. Constrained non-linear programming
KKT conditions
Barrier method and Penalty functions
3. Lot Sizing
EOQ model
Wagner-Whitin Algorithm
Zangwill Algorithm
4. Job Shop Scheduling
Exact methods, Carlier-Pinson Algorithm
Euristhic methods, Nowicki-Smutnicki Algorithm
5. Vehicle Routing Problem
6. Crew Scheduling
7. Plant Location
Core Documentation
Lecture notesType of delivery of the course
Classroom lectures and exercises.Type of evaluation
The exam consists of two steps. In the written part the student is asked to solve two exercises. The oral part consists of one or more questions on the written part and/or theoretical questions. The exams of the last years are available on the web page of the course (http://pacciarelli.dia.uniroma3.it/CORSI/MSP/Welcome.html ).