Paper Info
Title
Bootstrapping to solve the limited data problem in production control: an application in batch process industries
Abstract
Batch process industries are characterized by complex precedence relationships among operations, which makes the estimation of an acceptable workload very difficult. Previous research indicated that a regression-based model that uses aggregate job set characteristics may be used to support order acceptance decisions. Applications of such models in real- life assume that sufficient historical data on job sets and the corresponding makespans are available. In practice, however, historical data maybe very limited and may not be sufficient to produce accurate regression estimates. This paper shows that such a lack of data significantly impacts the performance of regression-based order acceptance procedures. To resolve this problem, we devised a method that uses the bootstrap principle. A simulation study shows that performance improvements are obtained when using the parameters estimated from the bootstrapped data set, demonstrating that this bootstrapping procedure can indeed solve the limited data problem in production control.
Year
DOI
Venue
2006
10.1057/palgrave.jors.2601966
Journal of The Operational Research Society
Keywords
Field
DocType
batch process,production,reliability,communications technology,parameter estimation,location,scheduling,project management,computer science,logistics,forecasting,information technology,inventory,investment,marketing,operational research,information systems,operations research,management science
Production manager,Job shop scheduling,Production control,Regression analysis,Bootstrapping,Computer science,Workload,Batch processing,Bootstrapping (electronics),Operations management
Journal
Volume
Issue
ISSN
57
1
0160-5682
Citations 
PageRank 
References 
12
0.84
2
Authors
4
Name
Order
Citations
PageRank
VC Iva ˘ nescu1120.84
J. W. M. Bertrand2456.47
Jan C. Fransoo314015.31
J. P. C. Kleijnen45810.96