The students who complete this course will gain knowledge on fundamental concepts in modeling, control, and optimization of industrial production systems. Several components of actual production systems will be analyzed, and software tools for their modeling, control, and optimization, will be introduced. At the end of this course, students will gain knowledge on the terminology, role and working principles of the main components of modern production systems, as well as on the factors that determine their performance. Students will also be able to identify key issues in modeling and optimization of such systems, as well as to use software tools for modeling, simulation, and optimization. Most of the theoretical sections will be complemented by laboratory experiences to elucidate practical issues and let students familiarize with software tools for modeling, control, and optimization of the systems under exam. Notably, students who complete this course will acquire the following knowledge: - Understanding the main components, structures and layouts of automated production plants, as well as of stocking areas and transportation systems. - Understanding the main metrics used to evaluate the performance of production systems, e.g., cycle time, production rate, availability, etc. - Understanding the issues connected to the selection of industrial robots components for applications, such as grippers, vision systems, or robotic cells. - Understanding the structure of most common CNC machines, with the use of CAD-CAM programming and their related G-code simulations or other languages. - Understanding the PLC architecture and the main programming techniques for automation applications. - Understanding statistical methods and numerical algorithms to assess the performance of queuing systems. - 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 methods and algorithms for production planning: resource allocation, assignment, distribution. - Understanding the basic characteristics of artificial vision systems for industrial applications. Students who complete this course will acquire the following abilities: - Acquiring the terminology related to modern production systems. - Assessing the performance indices of production systems, devising strategies identify factors that limit productivity and designing method for performance improvement and optimization. - Analyzing the performance of robot components for applications, such as vision systems, grippers or robotic cells. - Interpreting and modifying machining codes for CNC machines, using simple CAD-CAM software tools to design workpiece machining. - Designing and implementing simple programs for PLCs, able to tackle real-world applications. - Modeling sections of production systems through queueing systems, assessing their performance, and identifying bottlenecks and margins for performance improvement. - Modeling scheduling problems for different production systems, and solving them, finding optimal or suboptimal solutions, through heuristic or linear programming approaches. - Using the main functions of artificial vision systems in industrial applications.

Basic to intermediate knowledge of MATLAB is 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.

- Overview of manufacturing systems, manufacturing operations, manufacturing metrics and economics. - CNC Machines overview, introduction to programming with G-codes. Use of CAD-CAM based simulators for the generation of the G-codes. - Complementary topics in robotics for industry; e.g., selection of grippers, performance assessment of robotic cells, vision systems, use of simulation tools to simulate robots (e.g., the Matlab Robotics Toolbox by Peter Corke). - Overview of material handling components. - Simulation of industrial production systems and their control in real time with PLCs. - 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. - The main scheduling problems of automated production systems: single machine, parallel machines, flow shop and job shop. Solutions obtained by known rules when applicable or, in general, by numerical algorithms. - Methods and algorithms for production planning: resource allocation, assignment and distribution. - Conclusive remarks and exam simulation

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 laboratory activities, 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. Textbooks are not mandatory. Selected parts may be useful to understand more in-depth the concepts reported in handouts and notes. The course has been prepared mainly based on the following textbooks. 1. M.P. Groover, “Automation, Production Systems, and Computer-Integrated Manufacturing”, Pearson 2. H. Jack, “Automating Manufacturing Systems with PLCs”, free download at http://claymore.engineer.gvsu.edu/~jackh/books.html 3. P. Monkman et al., “Robot Grippers”, Wiley 4. C.G. Cassandras, S. Lafortune, “Indtroduction to Discrete Event Systems”, Springer 5. M.L. Pinedo, “Scheduling – Theory, Algorithms, and Systems”, Springer

Lecture slides; Text book; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions; Video lectures (previous years); Simulation tools;

You can take this exam before attending the course

Exam: Computer lab-based test; Written test;

Specific learning outcomes are assessed through the administration of a final exam, either in a written form executed in the classroom, or using the moodle-based "Exam" platform in the Politecnico laboratories, according to the availability of Politecnico's facilities. Knowledge items will be assessed through the administration of quizzes, theoretical exercises, and problems concerning the description of the working principles of production systems. Abilities will be assessed through the administration of numerical exercises and problems, as well as through the design, analysis, or modification of software elements, written in the programming languages studied in class. The test consists of two sections: - the first section comprises 10 to 30 multiple-choice questions; - the second section comprises 2 to 5 problems. Such problems can be open-answer questions, numerical problems, coding problems. Students who do not attain a sufficient grade in the first section will not be admitted to the second section. Exact thresholds for passing the first section will be communicated during the first day of classes. The exam duration is up to 2 hours. Fixed times may be allotted for the first and the second part, respectively (e.g., first part to be executed in 30 minutes, second part to be executed in 1h30 minutes). Exact duration of the exam will be communicated in advance before the first session and will remain constant for the whole academic year. The exam is closed-books, closed-notes. Particularly hard-to-be-recalled formulas will be collected by the instructor in a cheat-sheet, which will be provided to students during the semester, at least two weeks before the first exam date. Alternatively, at least two weeks before the exam, the instructor will provide detailed instructions to students on how to compile a cheat-sheet. Such a cheat-sheet will be the only additional source of information that can be consulted during the exams. Simple calculators will be allowed. Tablets and cellular phones will be banned from exams. Incentives and penalties on the final marks may be given to students, in an amount not greater than the +/-10% (+/-3 pts), based on the active participation to the laboratory activities. The active participation to laboratory activities may be measured with the administration of tests or exercises. Exact evaluation criteria will be communicated during the first day of classes. Honors (lode) will be granted to tests executed to perfection, also in terms of explanation and presentation of the solutions of the open-answer problems. An additional oral examination may only be requested by the instructor, in order to better assess the student’s preparation. The course may enroll students from other MSc courses (e.g., so far, Electronics Engineering students), in a coordinated agreement with the MSc course coordinator. In this case, a different syllabus may be devised, together with customized evaluation procedures. All the details in this regard will be provided to students in the first week of the course.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.