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010   $a3-319-53321-5
024 7 $a10.1007/978-3-319-53321-6
035   $a(CKB)3710000001127269
035   $a(DE-He213)978-3-319-53321-6
035   $a(MiAaPQ)EBC4832550
035   $a(PPN)199770123
035   $a(EXLCZ)993710000001127269
100   $a20170328d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aRobust Modelling and Simulation $eIntegration of SIMIO with Coloured Petri Nets /$fby Idalia Flores De La Mota, Antoni Guasch, Miguel Mujica Mota, Miquel Angel Piera
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XVII, 162 p. 112 illus., 70 illus. in color.) 
311   $a3-319-53320-7 
320   $aIncludes bibliographical references at the end of each chapters.
327   $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.
330   $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).
606   $aIndustrial engineering
606   $aProduction engineering
606   $aOperations research
606   $aManagement science
606   $aComputer simulation
606   $aComputer mathematics
606   $aIndustrial and Production Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T22008
606   $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024
606   $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000
606   $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026
615  0$aIndustrial engineering.
615  0$aProduction engineering.
615  0$aOperations research.
615  0$aManagement science.
615  0$aComputer simulation.
615  0$aComputer mathematics.
615 14$aIndustrial and Production Engineering.
615 24$aOperations Research, Management Science.
615 24$aSimulation and Modeling.
615 24$aComputational Science and Engineering.
676   $a670
700   $aDe La Mota$b Idalia Flores$4aut$4http://id.loc.gov/vocabulary/relators/aut$0861047
702   $aGuasch$b Antoni$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aMujica Mota$b Miguel$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aAngel Piera$b Miquel$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910254342103321
996   $aRobust Modelling and Simulation$91921604
997   $aUNINA