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010   $a3-319-55810-2
024 7 $a10.1007/978-3-319-55810-3
035   $a(CKB)3710000001388406
035   $a(DE-He213)978-3-319-55810-3
035   $a(MiAaPQ)EBC4863252
035   $a(PPN)201474727
035   $a(EXLCZ)993710000001388406
100   $a20170519d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplied Simulation and Optimization 2 $eNew Applications in Logistics, Industrial and Aeronautical Practice /$fedited by Miguel Mujica Mota, Idalia Flores De La Mota
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XIV, 282 p. 127 illus., 107 illus. in color.) 
311   $a3-319-55809-9 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aPreface -- Introduction -- Supply Chain Problems -- Logistics Problems -- Manufacturing Problems -- Aeronautical Problems -- Big Data Applications -- Conclusions -- Bibliography -- References.
330   $aBuilding on the author?s earlier Applied Simulation and Optimization, this book presents novel methods for solving problems in industry, based on hybrid simulation-optimization approaches that combine the advantages of both paradigms. The book serves as a comprehensive guide to tackling scheduling, routing problems, resource allocations and other issues in industrial environments, the service industry, production processes, or supply chains and aviation. Logistics, manufacturing and operational problems can either be modelled using optimization techniques or approaches based on simulation methodologies. Optimization techniques have the advantage of performing efficiently when the problems are properly defined, but they are often developed through rigid representations that do not include or accurately represent the stochasticity inherent in real systems. Furthermore, important information is lost during the abstraction process to fit each problem into the optimization technique. On the other hand, simulation approaches possess high description levels, but the optimization is generally performed through sampling of all the possible configurations of the system. The methods explored in this book are of use to researchers and practising engineers in fields ranging from supply chains to the aviation industry. .
606   $aComputer mathematics
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aComputer simulation
606   $aIndustrial engineering
606   $aProduction engineering
606   $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026
606   $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006
606   $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000
606   $aIndustrial and Production Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T22008
615  0$aComputer mathematics.
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615  0$aComputer simulation.
615  0$aIndustrial engineering.
615  0$aProduction engineering.
615 14$aComputational Science and Engineering.
615 24$aMathematical and Computational Engineering.
615 24$aSimulation and Modeling.
615 24$aIndustrial and Production Engineering.
676   $a004
702   $aMujica Mota$b Miguel$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aFlores De La Mota$b Idalia$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910254279203321
996   $aApplied simulation and optimization 2$91562313
997   $aUNINA