1. SylabUZ
2. Faculty of Engineering and Technical Sciences
3. winter term 2021/2022
4. Management and Production Engineering - Second-cycle studies leading to MSc degree
5. Modelling and simulation of production processes

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Modelling and simulation of production processes - course description

Aim of the course

This is the transfer of basic knowledge and the acquisition of skills and competences in modelling and in the simulation of production processes; these will be used in further education and will be most useful in future, professional work.

Prerequisites

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Probability calculus, the basis of Computer Science, the organisation of production systems.

Scope

Lecture

Basic issues concerning conventional and flexible production systems. Model, modelling and simulation, simulation tools - a review. Mathematical programming issues in modelling. Methods for describing discrete processes. The theory of ‘mass handling’: single-and multi-channel systems; queue-free systems with queues; application intensity depending on the status of the system; optimisation in queue systems, the ‘Monte-Carlo’ method. Petri networks: action networks; position/transit networks; a formal description of networks; graph of reachable states; invariants; network viability and blocking; coloured networks; applications.

Laboratory

The real process versus the conceptual model. Linear discreet optimisation (integer) and zero-one programming methods for modelling production processes. ‘Queue system’ methods in modelling. Simulation of production processes, using queue networks. The ‘Monte-Carlo’ method and the generation of pseudo-random numbers. Simulation of simple and complex processes using Petri networks.

Teaching methods

Lecture: a conventional lecture.

Laboratory: Laboratory work using available computer programmes.

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Lecture: graded credit. The rating is issued based on a written exam covering the verification of the knowledge of the issues from the curriculum.

Laboratory: graded credit. The rating is determined based on the evaluation of skills related to the performance of laboratory tasks.

Final rating: the arithmetical mean of grades from individual types of classes.

Recommended reading

1. Chung C.A.: Simulation modeling handbook : a practical approach, CRC Press, 2004.
2. Hillier F.S., Lieberman G.J., Introduction to Operations Research, McGrawHill, 2015.
3. Thomopoulos N.T.: Fundamentals of Queuing Systems, Springer, New York, 2012.
4. Zhou M.C., Venkatesh K. Modeling, simulation and control of flexible manufacturing systems: a Petri net approach. World Scientific, 1999.
5. Electronic help of programs

Further reading

1. Barczyk J., Automatyzacja systemów dyskretnych, Oficyna Politechniki Warszawskiej, Warszawa 2003
2. Sawik T. Optymalizacja dyskretna w elastycznych systemach produkcyjnych WNT Warszawa, 1992
3. Morrison F., Sztuka modelowania układów dynamicznych: deterministycznych, chaotycznych, stochastycznych, WNT, Warszawa 1996.
4. Hillier F.S., Lieberman G.J., Introduction to Operations Research, McGrawHill.

Notes

Modified by prof. dr hab. Taras Nahirnyy (last modification: 04-05-2021 17:05)