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Analytical Evaluation of Hierarchical Planning Systems

Author

Listed:
  • M. A. H. Dempster

    (Balliol College, Oxford, England)

  • M. L. Fisher

    (University of Pennsylvania, Philadelphia, Pennsylvania)

  • L. Jansen

    (Mathematisch Centrum, Amsterdam, The Netherlands)

  • B. J. Lageweg

    (Mathematisch Centrum, Amsterdam, The Netherlands)

  • J. K. Lenstra

    (Mathematisch Centrum, Amsterdam, The Netherlands)

  • A. H. G. Rinnooy Kan

    (Erasmus University, Rotterdam, The Netherlands)

Abstract

Hierarchical planning systems have become popular for multilevel decision problems. After reviewing the concept of hierarchical planning and citing some examples, we describe a method for analytic evaluation of a hierarchical planning system. We show that multilevel decision problems can be nicely modeled as multistage stochastic programs. Then any hierarchical planning system can be measured against the yardstick of optimality in this stochastic program. We demonstrate this approach on a hierarchical system that can be shown to be asymptotically optimal for a job shop design/scheduling problem.

Suggested Citation

  • M. A. H. Dempster & M. L. Fisher & L. Jansen & B. J. Lageweg & J. K. Lenstra & A. H. G. Rinnooy Kan, 1981. "Analytical Evaluation of Hierarchical Planning Systems," Operations Research, INFORMS, vol. 29(4), pages 707-716, August.
    • Handle: RePEc:inm:oropre:v:29:y:1981:i:4:p:707-716
    DOI: 10.1287/opre.29.4.707
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    Citations

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    Cited by:

    1. Malladi, Satya S. & Christensen, Jonas M. & Ramírez, David & Larsen, Allan & Pacino, Dario, 2022. "Stochastic fleet mix optimization: Evaluating electromobility in urban logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    2. Mingxing Li & Ray Y. Zhong & Ting Qu & George Q. Huang, 2022. "Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1355-1372, June.
    3. Gyana R. Parija & Shabbir Ahmed & Alan J. King, 2004. "On Bridging the Gap Between Stochastic Integer Programming and MIP Solver Technologies," INFORMS Journal on Computing, INFORMS, vol. 16(1), pages 73-83, February.
    4. Dehayem Nodem, F.I. & Kenne, J.P. & Gharbi, A., 2009. "Hierarchical decision making in production and repair/replacement planning with imperfect repairs under uncertainties," European Journal of Operational Research, Elsevier, vol. 198(1), pages 173-189, October.
    5. Pesenti, Raffaele, 1995. "Hierarchical resource planning for shipping companies," European Journal of Operational Research, Elsevier, vol. 86(1), pages 91-102, October.
    6. Giovanni Pantuso & Kjetil Fagerholt & Stein W. Wallace, 2015. "Solving Hierarchical Stochastic Programs: Application to the Maritime Fleet Renewal Problem," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 89-102, February.
    7. Stougie, Leen & Vlerk, Maarten H. van der, 2003. "Approximation in stochastic integer programming," Research Report 03A14, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    8. Samaddar, Subhashish & Rabinowitz, Gad & Zhang, Guoqiang Peter, 2005. "An experimental analysis of solution performance in a resource sharing and scheduling problem," European Journal of Operational Research, Elsevier, vol. 165(1), pages 139-156, August.
    9. Selcuk, B. & Fransoo, J.C. & De Kok, A.G., 2006. "The effect of updating lead times on the performance of hierarchical planning systems," International Journal of Production Economics, Elsevier, vol. 104(2), pages 427-440, December.
    10. repec:dgr:rugsom:03a14 is not listed on IDEAS
    11. Julia Pahl & Stefan Voß & David Woodruff, 2007. "Production planning with load dependent lead times: an update of research," Annals of Operations Research, Springer, vol. 153(1), pages 297-345, September.

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