Bootstrapping to solve the limited data problem in production control: an application in batch process industries

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.

[1]  Robert Tibshirani,et al.  An Introduction to the Bootstrap CHAPMAN & HALL/CRC , 1993 .

[2]  Charles H. Smith,et al.  Selecting allowance policies for improved job shop performance , 1993 .

[3]  Jacques Carlier,et al.  Scheduling jobs with release dates and tails on identical machines to minimize the makespan , 1987 .

[4]  T.C.E. Cheng,et al.  Survey of scheduling research involving due date determination decisions , 1989 .

[5]  J. Brian Gray,et al.  Introduction to Linear Regression Analysis , 2002, Technometrics.

[6]  J. Will M. Bertrand,et al.  The performance of workload rules for order acceptance in batch chemical manufacturing , 2000, J. Intell. Manuf..

[7]  G. Ragatz,et al.  A simulation analysis of due date assignment rules , 1984 .

[8]  V. Cristina Ivanescu,et al.  Makespan estimation and order acceptance in batch process industries when processing times are uncertain , 2002, OR Spectr..

[9]  Michael S. Lane,et al.  Operations Research Techniques: A Longitudinal Update 1973–1988 , 1993 .

[10]  Robert H. Storer,et al.  Robustness Measures and Robust Scheduling for Job Shops , 1994 .

[11]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[12]  Han Hoogeveen,et al.  Scheduling multipurpose batch process industries with no-wait restrictions by simulated annealing , 2000, Eur. J. Oper. Res..

[13]  Kevin J. Dooley,et al.  Mixing static and dynamic flowtime estimates for due-date assignment , 1993 .

[14]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[15]  J. Will M. Bertrand,et al.  Using aggregate estimation models for order acceptance in a decentralized production control structure for batch chemical manufacturing , 2000 .

[16]  Kevin J. Dooley,et al.  Dynamic rules for due-date assignment , 1991 .