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000823350 019__ $$a995357773$$a999484412$$a999690346
000823350 020__ $$a9783658191207$$q(electronic book)
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000823350 0247_ $$a10.1007/978-3-658-19120-7$$2doi
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000823350 049__ $$aISEA
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000823350 1001_ $$aHübl, Alexander,$$eauthor.
000823350 24510 $$aStochastic modelling in production planning :$$bmethods for improvement and investigations on production system behaviour /$$cby Alexander Hübl.
000823350 264_1 $$aWiesbaden :$$bSpringer Gabler,$$c2018.
000823350 300__ $$a1 online resource (xv, 139 pages) :$$billustrations
000823350 336__ $$atext$$btxt$$2rdacontent
000823350 337__ $$acomputer$$bc$$2rdamedia
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000823350 347__ $$atext file$$bPDF$$2rda
000823350 504__ $$aIncludes bibliographical references.
000823350 5050_ $$aUtilisation Concept -- Capacity Setting Methods -- Conwip -- Dispatching Rules.
000823350 506__ $$aAccess limited to authorized users.
000823350 520__ $$aAlexander Hübl develops models for production planning and analyzes performance indicators to investigate production system behaviour. He extends existing literature by considering the uncertainty of customer required lead time and processing times as well as by increasing the complexity of multi-machine multi-items production models. Results are on the one hand a decision support system for determining capacity and the further development of the production planning method Conwip. On the other hand, the author develops the JIT intensity and analytically proves the effects of dispatching rules on production lead time. Contents Utilisation Concept Capacity Setting Methods Conwip Dispatching Rules Target Groups Researchers and students in the fields of logistics and operations management Practitioners in production planning, logistics, capacity planning The Author Alexander Hübl holds a PhD in logistics and operations management from University of Vienna, Austria. He leads the research group Supply Chain Planning at the department Logistikum at the University of Applied Sciences Upper Austria. His research interests include discrete event simulation, agent-based simulation, queuing theory, stochastic modelling and their applications in logistics and operations management.
000823350 588__ $$aVendor-supplied metadata.
000823350 650_0 $$aDecision making.
000823350 650_0 $$aOperations research.
000823350 650_0 $$aProduction planning.
000823350 650_0 $$aStochastic models.
000823350 650_0 $$aProduction management.
000823350 650_0 $$aBusiness logistics.
000823350 77608 $$iPrint version:$$aHübl, Alexander.$$tStochastic modelling in production planning.$$dWiesbaden : Springer Gabler, 2018$$z3658191198$$z9783658191191$$w(OCoLC)994849183
000823350 852__ $$bebk
000823350 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-658-19120-7$$zOnline Access$$91397441.1
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000823350 980__ $$aEBOOK
000823350 980__ $$aBIB
000823350 982__ $$aEbook
000823350 983__ $$aOnline
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