SIMULATION OF PRODUSTION LOGISTICS SYSTEM
LEARNING OUTCOMES OF THE COURSE UNIT
Knowledge and understanding:
at the end of the course, students will have acquired the basic knowledge about the structure and functioning of a simulator, in terms of: analysis of the context to by reproduced; statistical analysis of the process data; tools available to develop a simulation model for the process analyzed; analysis of the data obtained by the simulation; optimization of operating leverages, to improve the performance of the process.
Applying knowledge and understanding:
students will be able to develop a process simulator, both in the logistic and production context, starting from the scenario to be analysed, by choosing the tool that better reproduces the process studied, identifying performance indices and operation leverages on which to act through "what if" analyses, to optimize the process or propose alternative process configurations.
supported by the results of the simulation campaigns, students will be able to evaluate the impact of design decisions and operational leverages on the performance of complex productive and logistic systems.
students should acquire the specific vocabulary related to simulation. It is expected that, at the end of the course, students will be able to communicate the main contents of the course both orally, in writing and through the implementation of ad hoc simulators, either supported by “general purpose” softwares or dedicated tools.
Students who have attended the course will be able to deepen their knowledge in the field of process simulation, by consulting specialized texts, or journal papers, or other sources, as well as new software packages (both "general purpose" or dedicated), also outside the topics covered in class.
There are no compulsory prerequisites.
COURSE CONTENTS SUMMARY
The aim of this class is to provide the basis knowledge related to the main aspects of simulation studies in the field of logistics and manufacturing systems, including modeling, simulation languages, validation and output data analysis.
A.M. Law and W.D. Kelton, Simulation modeling & analysis, McGraw-Hill, Inc.,
ASSESSMENT METHODS AND CRITERIA
The exam consists of:
- a written test, either in form of multiple choices or open questions. The test includes both theoretical questions and exercises
- a presentations and discussion of a project that is focused on a specific topic developed by a group a few students
- a practical test by use of software tools that are utilized during the course
Lectures. During the class, numerical examples concerning practice cases are shown. Several numerical example are solved by both general purpose software and dedicated software.
Basic Simulation Modeling (the nature of simulation; system, modes and simulation; discrete event simulation, distributed simulation, steps in a simulation study, other types of simulation; advantages, disadvantages and pitfall of simulation).
Review of basic probability and statistics.
Building valid and credible simulation models.
Selecting input probability distributions.
Output data analysis for a single system.
Comparing alternative system configuration.
Experimental design and optimization.
Inventory management by a simulation approach
Advanced utilization of MS Excel as general purpose tool for simulation
Utilization of Simul8 as a simulation tool for production systems
Utilization of Tdyn as a process simulation tool