Details
| Original language | English |
|---|---|
| Pages (from-to) | 7007-7026 |
| Number of pages | 20 |
| Journal | International Journal of Production Research |
| Volume | 52 |
| Issue number | 23 |
| Publication status | Published - 13 Jun 2014 |
Abstract
This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year.
Keywords
- capacity planning, mixed-integer linear programming, MRO
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
- Engineering(all)
- Industrial and Manufacturing Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: International Journal of Production Research, Vol. 52, No. 23, 13.06.2014, p. 7007-7026.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Decision models for capacity planning in a regeneration environment
AU - Eickemeyer, Steffen C.
AU - Herde, Felix
AU - Irudayaraj, Pratik
AU - Nyhuis, Peter
N1 - Funding information: The authors would like to thank the DFG research organisation for providing funding for this research project within the scope of the CRC 871 programme.
PY - 2014/6/13
Y1 - 2014/6/13
N2 - This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year.
AB - This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year.
KW - capacity planning
KW - mixed-integer linear programming
KW - MRO
UR - http://www.scopus.com/inward/record.url?scp=84908564062&partnerID=8YFLogxK
U2 - 10.1080/00207543.2014.923122
DO - 10.1080/00207543.2014.923122
M3 - Article
AN - SCOPUS:84908564062
VL - 52
SP - 7007
EP - 7026
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 23
ER -