Politecnico di Torino
Politecnico di Torino
   
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Politecnico di Torino
Academic Year 2015/16
01PECOV, 01PECQW
Software architecture for automation
Master of science-level of the Bologna process in Computer Engineering - Torino
Master of science-level of the Bologna process in Mechatronic Engineering - Torino
Teacher Status SSD Les Ex Lab Years teaching
Ghirardi Marco ORARIO RICEVIMENTO RC ING-INF/04 27 9 24 5
SSD CFU Activities Area context
ING-INF/04 6 B - Caratterizzanti Ingegneria informatica
ORA-01722: invalid number
Subject fundamentals
The course examines the automation software controlling a production system, which can be seen through several abstraction levels: logistics, long term planning, scheduling to the control of a single station. At each of those levels, a corresponding software must be able to receive and monitor the information on the state of the system , analyze them and to output commands in order to achieve a desired behavior, often optimizing one or more performance indexes.
This course is focused on the logistic and production planning levels, and its objective is to provide methods for modeling, simulating, and optimizing technical and economical performances indexes of logistic and production systems.
Expected learning outcomes
Simulative models of production systems development.
Modeling and solution methods for combinatorial optimization problems (mainly referring to production planning and scheduling problems)
Methods for production planning and control.
Prerequisites / Assumed knowledge
Suggested prerequisites are a basic knowledge of discrete event systems, linear programming models, and a good programming skill (C/C++ mainly).
Contents
- Automation, planning, scheduling.
- Scheduling theory basics.
- Simulation of logistic and production systems.
- Omnet++ (http://www.omnetpp.org/) : implementing simulation models.
- Combinatorial Optimization and Linear Programming models.
- Xpress software (http://www.fico.com/en/Products/DMTools/Pages/FICO-Xpress-Optimization-Suite.aspx) for solving PL models.
- Heuristic algorithms: greedy, local search, meta-heuristics (Tabu Search, Simulated Annealing, Genetic Algorithms, etc), Mat-heuristics.
- Specific scheduling problem examples.
- Multi-objective problems and Pareto analysis.
- Integration of an optimization-control software in a simulated system (dispatching rules, rolling horizon techniques, re-scheduling requests, etc).
- Real world application examples.