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

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  • V C Ivănescu

    (Technische Universiteit Eindhoven)

  • J W M Bertrand

    (Technische Universiteit Eindhoven)

  • J C Fransoo

    (Technische Universiteit Eindhoven)

  • J P C Kleijnen

    (Tilburg University)

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.

Suggested Citation

  • V C Ivănescu & J W M Bertrand & J C Fransoo & J P C Kleijnen, 2006. "Bootstrapping to solve the limited data problem in production control: an application in batch process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 2-9, January.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:1:d:10.1057_palgrave.jors.2601966
    DOI: 10.1057/palgrave.jors.2601966
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    References listed on IDEAS

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    1. Raaymakers, W. H. M. & Hoogeveen, J. A., 2000. "Scheduling multipurpose batch process industries with no-wait restrictions by simulated annealing," European Journal of Operational Research, Elsevier, vol. 126(1), pages 131-151, October.
    2. Carlier, Jacques, 1987. "Scheduling jobs with release dates and tails on identical machines to minimize the makespan," European Journal of Operational Research, Elsevier, vol. 29(3), pages 298-306, June.
    3. Michael S. Lane & Ali H. Mansour & John L. Harpell, 1993. "Operations Research Techniques: A Longitudinal Update 1973–1988," Interfaces, INFORMS, vol. 23(2), pages 63-68, April.
    4. Cheng, T. C. E. & Gupta, M. C., 1989. "Survey of scheduling research involving due date determination decisions," European Journal of Operational Research, Elsevier, vol. 38(2), pages 156-166, January.
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    Cited by:

    1. Alessandro Agnetis & Alberto Coppi & Matteo Corsini & Gabriella Dellino & Carlo Meloni & Marco Pranzo, 2014. "A decomposition approach for the combined master surgical schedule and surgical case assignment problems," Health Care Management Science, Springer, vol. 17(1), pages 49-59, March.
    2. Kleijnen, J.P.C., 2007. "Simulation Experiments in Practice : Statistical Design and Regression Analysis," Discussion Paper 2007-09, Tilburg University, Center for Economic Research.
    3. Kleijnen, J.P.C., 2006. "Generalized Response Surface Methodology : A New Metaheuristic," Discussion Paper 2006-77, Tilburg University, Center for Economic Research.
    4. Kleijnen, J.P.C., 2006. "White Noise Assumptions Revisited : Regression Models and Statistical Designs for Simulation Practice," Discussion Paper 2006-50, Tilburg University, Center for Economic Research.
    5. M I Reis dos Santos & P M Reis dos Santos, 2011. "Construction and validation of distribution-based regression simulation metamodels," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1376-1384, July.
    6. Slotnick, Susan A., 2011. "Order acceptance and scheduling: A taxonomy and review," European Journal of Operational Research, Elsevier, vol. 212(1), pages 1-11, July.

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