Reliable capacity planning despite uncertain disassembly, regeneration and reassembly workloads by using statistical and mathematical approaches - Validation in subsidiaries of a global MRO company with operations in Asia, Europe and North America

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dc.identifier.uri http://dx.doi.org/10.15488/913
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/937
dc.contributor.author Eickemeyer, Steffen C.
dc.contributor.author Steinkamp, Simon
dc.contributor.author Schuster, Bernhardt
dc.contributor.author Bodenhage, Frank
dc.contributor.author Nyhuis, Peter
dc.date.accessioned 2016-12-21T13:00:34Z
dc.date.available 2016-12-21T13:00:34Z
dc.date.issued 2014
dc.identifier.citation Eickemeyer, S.C.; Steinkamp, S.; Schuster, B.; Bodenhage, F.; Nyhuis, P.: Reliable capacity planning despite uncertain disassembly, regeneration and reassembly workloads by using statistical and mathematical approaches - Validation in subsidiaries of a global MRO company with operations in Asia, Europe and North America. In: Procedia CIRP 23 (2014), Nr. C, S. 252-257. DOI: https://doi.org/10.1016/j.procir.2014.10.097
dc.description.abstract The MRO industry faces substantial challenges with regard to the capacity planning of disassembly and reassembly work. This is due to the unknown workloads when regenerating complex investment goods and is caused, in particular, by the uncertain degree of disassembly and the complex challenges of reassembly. Forecasting techniques based on Bayesian networks are developed along with mathematical models which optimize capacity utilization, job order and the resulting costs. The approaches are tested and validated in conjunction with an MRO company with global operations. The results show possibilities for enhancing the planning processes and are found to be transferable on an international scale regardless of sociocultural and process differences. eng
dc.description.sponsorship DFG/CRC/871
dc.language.iso eng
dc.publisher Amsterdam : Elsevier
dc.relation.ispartofseries Procedia CIRP 23 (2014)
dc.rights CC BY-NC-ND 3.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject Bayesian networks eng
dc.subject Capacity planning eng
dc.subject Complex capital goods eng
dc.subject Damage library eng
dc.subject Data mining eng
dc.subject Disassembly eng
dc.subject Forecast eng
dc.subject Maintenance eng
dc.subject Mixed-integer linear programming eng
dc.subject MRO eng
dc.subject Complex networks eng
dc.subject Data mining eng
dc.subject Forecasting eng
dc.subject Integer programming eng
dc.subject Maintenance eng
dc.subject Capital goods eng
dc.subject Disassembly eng
dc.subject Mixed integer linear programming eng
dc.subject MRO eng
dc.subject Reassembly eng
dc.subject Regeneration eng
dc.subject Bayesian networks eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau ger
dc.subject.ddc 330 | Wirtschaft ger
dc.subject.ddc 510 | Mathematik ger
dc.title Reliable capacity planning despite uncertain disassembly, regeneration and reassembly workloads by using statistical and mathematical approaches - Validation in subsidiaries of a global MRO company with operations in Asia, Europe and North America
dc.type Article
dc.type Text
dc.relation.issn 22128271
dc.relation.doi https://doi.org/10.1016/j.procir.2014.10.097
dc.bibliographicCitation.issue C
dc.bibliographicCitation.volume 23
dc.bibliographicCitation.firstPage 252
dc.bibliographicCitation.lastPage 257
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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