000 03997nam a22006015i 4500
001 978-3-319-53321-6
003 DE-He213
005 20220801221109.0
007 cr nn 008mamaa
008 170328s2017 sz | s |||| 0|eng d
020 _a9783319533216
_9978-3-319-53321-6
024 7 _a10.1007/978-3-319-53321-6
_2doi
050 4 _aT55.4-60.8
072 7 _aTGP
_2bicssc
072 7 _aTEC009060
_2bisacsh
072 7 _aTGP
_2thema
082 0 4 _a670
_223
100 1 _aDe La Mota, Idalia Flores.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954101
245 1 0 _aRobust Modelling and Simulation
_h[electronic resource] :
_bIntegration of SIMIO with Coloured Petri Nets /
_cby Idalia Flores De La Mota, Antoni Guasch, Miguel Mujica Mota, Miquel Angel Piera.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVII, 162 p. 112 illus., 70 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- 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.
520 _aThis 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).
650 0 _aIndustrial engineering.
_931641
650 0 _aProduction engineering.
_93683
650 0 _aOperations research.
_912218
650 0 _aManagement science.
_98316
650 0 _aComputer simulation.
_95106
650 0 _aMathematics—Data processing.
_931594
650 1 4 _aIndustrial and Production Engineering.
_931644
650 2 4 _aOperations Research, Management Science .
_931720
650 2 4 _aComputer Modelling.
_954102
650 2 4 _aComputational Science and Engineering.
_954103
700 1 _aGuasch, Antoni.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954104
700 1 _aMujica Mota, Miguel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954105
700 1 _aAngel Piera, Miquel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_954106
710 2 _aSpringerLink (Online service)
_954107
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319533209
776 0 8 _iPrinted edition:
_z9783319533223
776 0 8 _iPrinted edition:
_z9783319851266
856 4 0 _uhttps://doi.org/10.1007/978-3-319-53321-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79288
_d79288