Details
| Original language | English |
|---|---|
| Pages (from-to) | 252-257 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 23 |
| Issue number | C |
| Publication status | Published - 29 Dec 2014 |
| Event | 5th CIRP Conference on Assembly Technologies and Systems, CATS 2014 - Dresden, Germany Duration: 12 May 2014 → 14 May 2014 |
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.
Keywords
- Bayesian networks, Capacity planning, Complex capital goods, Damage library, Data mining, Disassembly, Forecast, Maintenance, Mixed-integer linear programming, MRO, Reassembly, Regeneration
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Procedia CIRP, Vol. 23, No. C, 29.12.2014, p. 252-257.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Reliable capacity planning despite uncertain disassembly, regeneration and reassembly workloads by using statistical and mathematical approaches
T2 - 5th CIRP Conference on Assembly Technologies and Systems, CATS 2014
AU - Eickemeyer, Steffen C.
AU - Steinkamp, Simon
AU - Schuster, Bernhardt
AU - Bodenhage, Frank
AU - Nyhuis, Peter
N1 - Funding information: The authors would like to thank the German Research Foundation (DFG) for providing funding for this research project within the scope of the CRC 871 program. The authors would also like to thank the Graduate Academy of Leibniz University Hannover for providing funding for the validation of the theoretical results in the global industry.
PY - 2014/12/29
Y1 - 2014/12/29
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - Capacity planning
KW - Complex capital goods
KW - Damage library
KW - Data mining
KW - Disassembly
KW - Forecast
KW - Maintenance
KW - Mixed-integer linear programming
KW - MRO
KW - Reassembly
KW - Regeneration
UR - http://www.scopus.com/inward/record.url?scp=84922746208&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2014.10.097
DO - 10.1016/j.procir.2014.10.097
M3 - Conference article
AN - SCOPUS:84922746208
VL - 23
SP - 252
EP - 257
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
IS - C
Y2 - 12 May 2014 through 14 May 2014
ER -