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UNIRSM Simulation of production systems

Simulation of production systems

Year

1

Semester

2

CFU

6

Professor

Roberto Montanari

Learning objectives

Knowledge and skills to be achieved
Knowledge and ability to understand
At the end of the course, the student must have acquired the main knowledge concerning the structure and operating principle of a simulator in terms of: analysis of the context that is intended to be reproduced virtually, statistical analysis of the data describing the process, tools capable of develop a simulator of the analyzed process, analysis of the data obtained from the simulation process and optimization of the operating levers in order to improve the performance of the process.

Expertise
The student must be able to develop a logistical and productive process simulator starting from the reality being analyzed, appropriately choosing the tool that is best able to virtually develop the process studied, identifying performance indices and the operational levers on which to act through “what if” analyzes to optimize operation and propose innovative solutions to those currently existing.

Judgment autonomy
The student must be able, supported by simulation campaigns, to evaluate the impact of decisions on the operational design levers on the performance of complex processes, both productive and logistical.

Communication skills
The student will have to acquire the specific vocabulary inherent to the simulation. It is expected that, at the end of the course, the student will be able to transmit, in oral form, in written form and also through the implementation of simulators both with "general purpose" languages ​​and with dedicated tools, the main contents of the course .

Learning ability
The student who has attended the course will be able to deepen their knowledge in the field of process simulation in general, through independent consultation of specialist texts, scientific or popular magazines, new software packages both "general purpose" and dedicated, including to outside of the topics strictly covered in class.

Course content

The course intends to provide criteria, methodologies, modeling and practical examples with the use of some simulation languages ​​of the main aspects of simulations within a logistical and production context

Basics of simulation (the nature of simulation; general definitions: systems, models, simulations; discrete event simulation; distributed simulation, steps in studying a simulation; other types of simulations; advantages, disadvantages of simulation).
Structuring reliable and reliable simulation processes.
Definition of input probability distributions.
Analysis of the results of a simulation experiment.
Comparison between different alternatives.
Fundamental principles of experiment design and optimization. Simulation of production systems and processes.
Simulation to support inventory management
Advanced use of MS Excel to support simulation
Using Simul8 in the simulation of production systems

Prerequisites

There are no mandatory preparatory requirements.

Bibliography

The slides projected during the course in PDF format and all the material used during the lessons and exercises (transparencies, plant diagrams, Excel sheets) are made available to the students and shared in a DropBox folder. To be added to the sharing, send an email to the teacher roberto.montanari@unipr.it with the subject “Shared IM Drive”.
In addition to the shared material, the student can personally delve deeper into some topics covered during the course by referring to the following texts:
• AM Law and WD Kelton, Simulation modeling & analysis, McGraw-Hill, Inc.
Those who would like to delve deeper into the characteristics of the transformations that digital technologies are inducing or can induce in communication and training processes in order to improve the quality of learning, as well as integrate technologies into teacher training, both initial, in the various university courses and continuously , they can consult the following texts:
Teaching and digital technologies. Methodologies, tools, paths. Antonio Marzano. Publisher: Pensa Multimedia
Laboratory teaching. Manual of good practices. What to do, how to do it. Tommaso Montefusco. Publisher: Edizioni Dal Sud

Teaching methods and tools

The course has a weight of 6 CFU, which corresponds to 48 hours of lessons. The teaching activities will be conducted by privileging frontal lessons in the computer lab alternating with exercises. During the frontal lessons the topics of the course are addressed from a theoretical-design point of view, in order to promote a deep understanding of the topics and to bring out any pre-knowledge on the topics in question by the trainees.
During the exercises carried out in class, during which it is possible to use the laboratory's calculation tools, students will be required to apply the theory to an exercise, a real case study or a project developed according to the methodological criteria illustrated in the lessons and in the bibliographical and teaching material.
To complement the teaching methods presented so far, if conditions allow, seminars are organized held by managers of multinational companies who report concrete experiences gained in real case studies.
The slides and notes used to support the lessons will be uploaded at the beginning of the course on the Elly platform and on a shared folder on DropBox. To download the slides from Elly, you must register for the online course, while to be added to the sharing, you must send an email to the teacher roberto.montanari@unipr.it with the subject “Shared SSP”.
Notes, slides, spreadsheets, tables and all shared material are considered an integral part of the teaching material. Non-attending students are reminded to check the available teaching material and the instructions provided by the teacher via the Elly platform, the only communication tool used for direct teacher/student contact.

Assessment methods and criteria

The learning assessment involves one or more simulations of production contexts on the PC. The test normally consists of 5/10 questions (with equal weight of 1). The final mark is calculated by assigning each question a rating from 0 to 30 and carrying out the weighted average of the individual ratings, with final rounding up; the test is passed if it reaches a score of at least 18 points. Honors are awarded if the maximum score is achieved on each item to which mastery of the disciplinary vocabulary is added.

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