1.

Record Nr.

UNINA9910254342103321

Autore

De La Mota Idalia Flores

Titolo

Robust Modelling and Simulation : Integration of SIMIO with Coloured Petri Nets / / by Idalia Flores De La Mota, Antoni Guasch, Miguel Mujica Mota, Miquel Angel Piera

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017

ISBN

3-319-53321-5

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (XVII, 162 p. 112 illus., 70 illus. in color.)

Disciplina

670

Soggetti

Industrial engineering

Production engineering

Operations research

Management science

Computer simulation

Computer mathematics

Industrial and Production Engineering

Operations Research, Management Science

Simulation and Modeling

Computational Science and Engineering

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references at the end of each chapters.

Nota di contenuto

Preface -- Introduction -- Chapter 1: Introduction to Digital Simulation.-Chapter 2: Statistics elements for simulation -- Chapter 3: Modelling of Systems using Petri Nets -- Chapter 4: Integrating Coloured Petri Nets with SIMIO -- Chapter 5: Modelling Example -- References -- Annex.

Sommario/riassunto

This book presents for the first time a methodology that combines the power of a modelling formalism such as colored petri nets with the flexibility of a discrete event program such as SIMIO. Industrial practitioners have seen the growth of simulation as a methodology for tacking problems in which variability is the common denominator. Practically all industrial systems, from manufacturing to aviation are considered stochastic systems. Different modelling techniques have



been developed as well as mathematical techniques for formalizing the cause-effect relationships in industrial and complex systems. The methodology in this book illustrates how complexity in modelling can be tackled by the use of coloured petri nets, while at the same time the variability present in systems is integrated in a robust fashion. The book can be used as a concise guide for developing robust models, which are able to efficiently simulate the cause-effect relationships present in complex industrial systems without losing the simulation power of discrete-event simulation. In addition SIMIO’s capabilities allows integration of features that are becoming more and more important for the success of projects such as animation, virtual reality, and geographical information systems (GIS).