Stochastic Modelling in Production Planning Methods for Improvement and Investigations on Production System Behaviour /

Alexander 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-m...

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Κύριος συγγραφέας: Hübl, Alexander (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Gabler, 2018.
Έκδοση:1st ed. 2018.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03328nam a2200493 4500
001 978-3-658-19120-7
003 DE-He213
005 20191025152114.0
007 cr nn 008mamaa
008 170726s2018 gw | s |||| 0|eng d
020 |a 9783658191207  |9 978-3-658-19120-7 
024 7 |a 10.1007/978-3-658-19120-7  |2 doi 
040 |d GrThAP 
050 4 |a HD30.23 
072 7 |a KJT  |2 bicssc 
072 7 |a BUS049000  |2 bisacsh 
072 7 |a KJT  |2 thema 
072 7 |a KJMD  |2 thema 
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100 1 |a Hübl, Alexander.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
245 1 0 |a Stochastic Modelling in Production Planning  |h [electronic resource] :  |b Methods for Improvement and Investigations on Production System Behaviour /  |c by Alexander Hübl. 
250 |a 1st ed. 2018. 
264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Gabler,  |c 2018. 
300 |a XV, 139 p. 19 illus., 12 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Utilisation Concept -- Capacity Setting Methods -- Conwip -- Dispatching Rules. 
520 |a Alexander 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. . 
650 0 |a Operations research. 
650 0 |a Decision making. 
650 0 |a Business logistics. 
650 0 |a Production management. 
650 1 4 |a Operations Research/Decision Theory.  |0 http://scigraph.springernature.com/things/product-market-codes/521000 
650 2 4 |a Logistics.  |0 http://scigraph.springernature.com/things/product-market-codes/519020 
650 2 4 |a Production.  |0 http://scigraph.springernature.com/things/product-market-codes/519010 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783658191191 
776 0 8 |i Printed edition:  |z 9783658191214 
856 4 0 |u https://doi.org/10.1007/978-3-658-19120-7  |z Full Text via HEAL-Link 
912 |a ZDB-2-BUM 
950 |a Business and Management (Springer-41169)