Risorse bibliografiche
Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2018/2019
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Docente Matta Andrea
Cfu 8.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento

Obiettivi dell'insegnamento

The course will allow students to analyze the performance of complex manufacturing systems using simulation models.

Risultati di apprendimento attesi

The course consists of lectures and classwork modules on presence. A classwork module will be delivered in Computer Lab to allow students to use state-of-the-art software for simulation of manufacturing systems. In Physical Lab students will be required to make experiments oriented to accomplish problem solving activities in the Project Work.

 Lectures sessions will allow students to have the basic knowledge and understanding of:

  • the main elements of integrated manufacturing systems and their relationships
  • the basic principles of discrete event simulation
  • the basic analysis methodologies in the context of simulation.

Classwork in computer laboratory sessions will allow students to apply knowledge and understanding:

  • students will model several integrated manufacturing systems using discrete event simulation software: manufacturing lines, assembly lines, flexible manufacturing systems
  • students will set up simulation models with data input analysis techniques
  • students will understand system behavior with data output analysis techniques
  • students will rank and compare alternative manufacturing systems using simulation outputs.

In the Project Work activities, students will develop the ability to handle complexity of manufacturing systems, to integrate knowledge acquired in other courses on productions systems and industrial plants, to formulate judgements with incomplete and uncertain data, to study in a manner that may be largely self-directed and/or autonomous. Students will also develop the ability to communicate their choices and conclusions to specialist audiences. Specifically, Project Work activities will allow students to:

  • Autonomously analyze and design an integrated manufacturing system in a context of partial information
  • Obtain data and acquiring knowledge from experiments in a physical laboratory
  • Choose modeling detail level from physical system to conceptual model
  • Choose computer coding strategies for building simulation models in a software platform
  • Summarize and present the results with technical documents and oral presentations.

Argomenti trattati

Base elements of integrated manufacturing systems. Students will learn the base elements of integrated manufacturing systems as well as their performance measures, abstraction levels, and design criteria.

List of contents - Introduction to integrated manufacturing systems. Main elements: stations, buffers, transporters, tools, fixtures, and pallets. Flexible Manufacturing Systems. Machining lines. Assembly lines. Job shops. Performance indicators. Relevant design variables.

Simulation for integrated manufacturing systems. Students will learn simulation techniques for estimating the performance indicators that are critical in the design and management of integrated manufacturing systems.

List of contents - Types of simulation: discrete event simulation, continuous simulation, agent based simulation. Entity Relationship Graphs. Random number generation. Random variate generation. Input modeling. Output analysis. Verification and validation. Selection of alternatives. Variance reduction using common random numbers. Management of simulation projects.


Computer Lab. Students will receive the essential theoretical and practical elements for creating and executing, in a dedicated software environment, valid simulation models representing integrated manufacturing systems. Students will also be supported by tutorial videos developed ad hoc for this course. List of contents – Arena software environment. Process oriented modeling concepts. Controlling pseudo-random numbers in simulation experiments. Collecting simulation statistics. Reading/writing from external sources. Implementing common random numbers technique. Comparison of alternatives.

Project Lab is integral part of the course. The objective of projects is to help students in applying the approaches and principles we teach in class. Project will be assigned through the semester. Project results are expected to be released before the first exam session, the specific deadline will be defined by the time the project will be assigned. The evaluation of project will be based on the produced Project Report and on a presentation. The project laboratory will require Experimental Lab sessions.

Experimental Lab. Students will be required to study and improve a manufacturing system. List of contents – Data collection, conceptual modeling, model validation.


Students are required to know the principles and methods of basic inferential statistics. The topics that must be known are:

  • Statistical distributions: exponential, uniform, triangular, normal, beta, t-student, chi_square, Fisher, Bernoulli, Poisson.
  • Confidence Interval and Tests: general framework of confidence intervals, general framework of standard tests with two hypotheses (H0 and H1), p_value concept, test about one or two means, tests about one or two variances.
  • Test to assess normality.
  • Correlation and autocorrelation measures.

Basic knowledge of Microsoft Excel and Matlab will be requested for data analysis.

Modalità di valutazione

The exam includes: a written test, a project report with oral discussion.


Written Test

Students must solve 2 up to 4 numerical problems in computer room. Students will be tested on the acquired a) basic knowledge and understanding, b) ability to apply knowledge in different problems:

  • Modeling manufacturing systems using Entity Relationship Graphs: selection of detail level, definition of events, state variables, parameters, and collection of statistics
  • Numerical generation of pseudo-random numbers using specific criteria and methods
  • Data analysis for simulation input
  • Definition of simulation settings: calculation of required number of replications, replication length, detection of warmup
  • Calculation of confidence interval estimation from simulation output on means, probabilities, quantiles
  • Numerical comparison of alternatives using ranking and selection methods.

The test is “open book”, students can bring and use during the test their notes and books. Desktops in the computer room can be used to make calculations. The use of personal notebooks, mobile phones, tablets, etc is not allowed. Each problem will have a certain grade and the final test grade will be the sum of the problem grades. The minimum value 18/30 is required to pass the written test.


Project Report

Students (in teams of three members) will analyze and improve a manufacturing system. They will be required to study an existing manufacturing system in Experimental Lab, to understand the main problems and to propose a plan for improvement using simulation experiments (application of knowledge, making judgments, handling complexity). A Project Report will document how they have developed their work and what results they have achieved.

Students must describe in the report:

  • Problem identification.
  • System Models.
  • Input data analysis.
  • Model validation.
  • Simulation setting.
  • Experimental design (one or more).
  • Identification and comparison of alternatives.
  • Comparison of alternatives.

In some activities students will be completely autonomous (e.g., in selecting the methods and procedures to find out a limited set of good solution alternatives) in choosing the approach to adopt (learning skills). Ability to technical communicate will be assessed based on the clarity of the Project Report and effectiveness of a slide presentation with 10 minutes of duration (communication).

Each project presented by a team receives a grade. The grade can be different for the team members based on their contribution to the project report. The minimum value 18/30 is required to pass the project activities. Students with a “not pass” assessment are required to repeat the Project and the Experimental Lab when available, perhaps jumping to the next year, depending on the availability of the Experimental laboratory.


Final Grading

Students who have passed both Written Test and Project activities will be graded with the following criteria:

  • Written test 50%
  • Project 50%

30 cum laude (con lode) will be assigned to only students who have received 30/30 grade in both the Written Test and the Project Report, and have shown distinguished level of knowledge acquired, critical analysis and communication.

Risorsa bibliografica facoltativaAverill M. Law, Simulation Modeling and Analysis, Editore: Mc Graw Hill, Anno edizione: 2013, ISBN: 978-0-07-340132-4
Risorsa bibliografica facoltativaBarry L. Nelson, Foundations and Methods of Stochastic Simulation. A First Course, Editore: Springer, Anno edizione: 2013, ISBN: 978-1-4614-6159-3
Risorsa bibliografica facoltativaStewart Robinson, Simulation. The Practice of Model Development and Use, Editore: Palgrave MacMillan, Anno edizione: 2014, ISBN: 978-1-137-32802-1

Software utilizzato
Software Info e download Virtual desktop
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PC studente
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Altri corsi
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MICROSOFT Office Excel Professional Plus SI SI

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
Ore di studio autonome
Laboratorio Informatico
Laboratorio Sperimentale
Laboratorio Di Progetto
Totale 80:00 120:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.8 / 1.6.8
Area Servizi ICT