01PECOV, 01PECQW, 01PECSM

A.A. 2020/21

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Ingegneria Informatica (Computer Engineering) - Torino

Master of science-level of the Bologna process in Mechatronic Engineering (Ingegneria Meccatronica) - Torino

Master of science-level of the Bologna process in Data Science And Engineering - Torino

Course structure

Teaching | Hours |
---|---|

Lezioni | 36 |

Esercitazioni in laboratorio | 24 |

Teachers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
---|---|---|---|---|---|---|---|

Ghirardi Marco | Ricercatore | ING-INF/04 | 36 | 0 | 36 | 0 | 9 |

Teaching assistant

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

ING-INF/04 | 6 | B - Caratterizzanti | Ingegneria informatica |

2020/21

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.

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, production planning and detailed production scheduling levels, and its objective is to provide methods for modeling, simulating, and optimizing technical and economical performances indexes of logistic and production systems.

Learn to model and acquire knowledge about the main solution methods for combinatorial optimization problems.
Know how to effectively approach production planning and scheduling problems.
Know the basics methods for production control systems.
Learn to use a simulative model of an automated production system.

- Know how to model a combinatorial optimization problem through a linear programming model.
- Know the main solution methods and algorithms for combinatorial optimization problems, both exact and heuristic.
- Know how to define and effectively approach production planning and scheduling problems.
- Know the basics of discrete event simulation, with the goal of simulating a production systems.
- Learn to use a simulative model of an automated production system and to implement a scheduler able to optimize the production efficiency.

Suggested prerequisites are a basic knowledge of discrete event systems, linear programming models, and a good programming skill (C/C++ mainly).

Suggested prerequisites are a basic knowledge of discrete event systems, linear programming models, and a good programming skill.

- 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.

- Introduction: Automation, planning, scheduling.
- Combinatorial Optimization and Linear Programming models with examples in production planning and logistics.
- Xpress software (https://www.fico.com/en/products/fico-xpress-optimization) for solving LP models.
- Scheduling theory basics.
- Simulation of logistic and production systems.
- Omnet++ (http://www.omnetpp.org/) : implementing and using simulation models.
- Combinatorial Optimization - exact algorithms.
- Combinatorial Optimization - heuristic algorithms: greedy, local search, meta-heuristics (Tabu Search, Simulated Annealing, Genetic Algorithms, etc), mat-heuristics.
- Specific production scheduling problem examples.
- Laboratory: integration of an optimization-control scheduling software in a simulated system.
- Real world application examples.

The course is based on classes, where theory and examples will be presented, and laboratory activities. In the laboratory two software will be used: Omnet++ for the implementation of simulation models, and FICO-Xpress as a tool for solving hard scheduling problems.

The course is based on classes, where theory and examples will be presented, and laboratory activities. In the laboratory two software will be used: Omnet++ for simulating production system models, and FICO-Xpress as a tool for solving hard scheduling problems. The students, organized in groups, will develop a scheduler and integrate it to the simulation model, with the goal of improving the production system efficiency.

The course is mainly based on the provided slides e course material.
Specific books for Discrete Event Simulation:
Carlucci, Menga, "Teoria dei sistemi ad eventi discreti", UTET 1998.
Cassandras, Lafortune, "Introduction to Discrete Event Systems", Springer.
G. Calafiore, "Elementi di Automatica", CLUT.
Specific books for Models and Algorithms for Combinatorial Optimization and Scheduling:
R. Tadei, F. Della Croce, "Elementi di Ricerca Operativa", Editrice Esculapio.
R. Tadei, F. Della Croce, A. Grosso, "Fondamenti di Ottimizzazione", Editrice Esculapio.
M. Ghirardi, A. Grosso, G. Perboli, "Esercizi di Ricerca Operativa", Editrice Esculapio.

The course is mainly based on the provided slides e course material.
Specific books for Models and Algorithms for Combinatorial Optimization and Scheduling:
M. Pinedo, "Scheduling: Theory, Algorithms, and Systems", Springer.
R. Tadei, F. Della Croce, "Elementi di Ricerca Operativa", Editrice Esculapio.
R. Tadei, F. Della Croce, A. Grosso, "Fondamenti di Ottimizzazione", Editrice Esculapio.
M. Ghirardi, A. Grosso, G. Perboli, "Esercizi di Ricerca Operativa", Editrice Esculapio.
Specific books for Discrete Event Simulation:
Cassandras, Lafortune, "Introduction to Discrete Event Systems", Springer.
G. Calafiore, "Elementi di Automatica", CLUT.
Carlucci, Menga, "Teoria dei sistemi ad eventi discreti", UTET 1998.

The exam is remotely done using the Exam platform, integrated with a proctoring software (Respondus), using Lockdown browser, and based on a set of multiple choice and open questions, with the objective of verifying the above mentioned competences (Expected Learning Outcomes). The exam duration is 1 hour. This exam gives a maximum of 20 points (minimum 10 for passing).
The laboratory activity will also be performed online, with personal and group goals which will be developed by the students on their personal PCs during the course, giving a maximum of 12 points.
The final evaluation is the sum of the points obtained with the exam and the laboratory project (with 31 being still 30, and 32 being 30L).

The exam is remotely done using the Exam platform, integrated with a proctoring software (Respondus), using Lockdown browser, and based on a set of multiple choice and open questions, with the objective of verifying the above mentioned competences (Expected Learning Outcomes). The exam duration is 1 hour. This exam gives a maximum of 20 points (minimum 10 for passing).
The laboratory activity will be performed online, with personal and group goals which will be developed by the students on their personal PCs during the course, giving a maximum of 12 points.
The final evaluation is the sum of the points obtained with the exam and the laboratory project (with 31 being still 30, and 32 being 30L).

The exam objective is to verify the above mentioned competences (Expected Learning Outcomes)
The onsite exam is written and it is composed by 2-3 numerical and modelling exercises. The duration of the exam is 1h30m and it is open book, while online it is remotely done using the Exam platform, integrated with a proctoring software (Respondus), using Lockdown browser, and based on a set of multiple choice and open questions. The exam duration is 1h30m. In both cases, the exam gives a maximum of 20 points (minimum 10 for passing).
A mandatory project based on the laboratory experiences gives a maximum of 12 points. The project is about developing a scheduler for a given productive system simulated model, with the goal of improving the production system efficiency. The project will be developed during the course, with personal and group goals which will be developed by the students in a Politecnico laboratory (if onsite) on their personal PCs (if online).
The final evaluation is the sum of the points obtained with the exam and the laboratory project (with 31 being still 30, and 32 being 30L).

The exam objective is to verify the above mentioned competences (Expected Learning Outcomes).
The onsite exam is written and it is composed by 2-3 numerical and modelling exercises. The duration of the exam is 1h30m and it is open book.
The online exam is remotely done using the Exam platform, integrated with a proctoring software (Respondus), using Lockdown browser, and based on a set of multiple choice and open questions. The exam duration is 1h30m.
In both cases, the exam gives a maximum of 20 points (minimum 10 for passing).
A mandatory project based on the laboratory experiences gives a maximum of 12 points. The project is about developing a scheduler for a given productive system simulated model, with the goal of improving the production system efficiency. The project will be developed during the course, with personal and group goals which will be developed by the students in a Politecnico laboratory (if onsite) on their personal PCs (if online).
The final evaluation is the sum of the points obtained with the exam and the laboratory project (with 31 being still 30, and 32 being 30L).

© Politecnico di Torino

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY