The course is entirely taught in English.
The course will address fundamental concepts of modeling for typical problems in automation and planning of production processes. Models and design techniques will focus on main components of an automated factory: tool machines, robot and vision systems, programmable logic controllers (PLC), and transportation systems.
All projects, based on practical case studies, will be tackled with the aid of software tools and the development of numerical examples.

Knowledge
• Understanding the main components, structures and layouts of automated production plants, as well as of stocking areas.
• Understanding how to use simulation tools to assess the performance of a factory transportation system.
• Understanding the kinematic structure of industrial robots and the techniques for the evaluation of their operational space.
• Understanding the structure of most common CNC machines, with the use of CAD-CAM programming and their related G-code simulations.
• Understanding the PLC architecture and the main programming techniques for automation applications.
• Understanding statistical methods and numerical techniques for reliability and quality control assessment.
• Understanding the main methods for the numerical solution of the most common production scheduling problems: single machine, parallel machines, flow shop, and job shop.
• Understanding statistical methods and numerical algorithms to assess the performance of queuing systems.
• Understanding methods and algorithms for production planning: resource allocation, assignment, distribution.

Abilities
• Selecting, using, managing, assessing, and integrating the most common modeling and simulation tools to tackle real problems in automated factories and transportation systems.

Basic to intermediate knowledge of MATLAB and C programming languages are required. Student lacking some background will be assisted in making it up through the suggestion of specific documentation, which has to be independently revised by the student.

• Handling and stowage systems simulated with Petri nets and controlled in real time by PLCs (1 Credit).
• CNC machines overview. Machine simulation of the G-code program produced by a CAD-CAM application (1 Credit).
• Evaluation of the operational space of a robot by computer simulation of the robot kinematics (1 Credit).
• Queuing theory applied to detect accumulation and bottleneck nodes of a production plant. Algorithms and statistical methods for reliability evaluation and quality control monitoring of production lots (1 Credit).
• The main scheduling problems of an automated production system: single machine, parallel machines, flow shop and job shop. Solutions obtained by known rules when applicable or, in general, by numerical algorithms (1 Credit).
• Methods and algorithms for production planning: resource allocation, assignment and distribution (1 Credit).

Theoretical foundations of each topic will be given in traditional in-class lectures. In-class lectures will be integrated by exercises, which will be partly solved in class and partly left to students for completion as homework. Each topic will be complemented by a laboratory activity, aiming at describing one or more tools for the analysis and simulation of the systems under exams (e.g., simulators for robots, CNC machines, PLC, scheduling solvers, etc.)

Handouts, notes, and program codes will be provided during the course. The instructor notes will include solved and unsolved exercises.

The exam consists of a written test, aimed at assessing the comprehension of the theoretical concepts and the ability to apply them to practical case studies.
The test consists of two sections:
- the first section comprises 10 to 15 multiple-choice questions;
- the second section comprises 3 to 5 problems. Such problems can be open-asnwer questions, numerical problems, coding problems.
The exam is closed-books, closed-notes. Students are allowed to take with them a single A4 sheet, compiled on both sides with notes at their wish.
The exam duration is 2h30m.
An additional oral examination may only be requested by the instructor, in order to better assess the student’s preparation.