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Stochastic Horizons for the Aggregate Planning Problem

Author

Listed:
  • Paul Kleindorfer

    (University of Pennsylvania)

  • Howard Kunreuther

    (University of Pennsylvania)

Abstract

This paper develops a methodology for showing how forecast horizons for stochastic planning problems relate to the planning procedures and the information system within the organization. To illustrate the approach we have chosen a relatively straightforward production problem where the firm can meet a fluctuating demand pattern through a combination of overtime and inventory-related options. We show that the optimal production plan is monotonically non-decreasing in the demand sequence so that bounds can be placed on the optimal first period production plan for an N-period problem. These bounds together with information and computationally-related costs are used in specifying a methodology for determining forecast horizons. An illustrative example suggests that such horizons are likely to be small in many realistic cases. The concluding section indicates how this methodology can be utilized for specifying stochastic horizons for more general aggregate planning decisions in organizations.

Suggested Citation

  • Paul Kleindorfer & Howard Kunreuther, 1978. "Stochastic Horizons for the Aggregate Planning Problem," Management Science, INFORMS, vol. 24(5), pages 485-497, January.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:5:p:485-497
    DOI: 10.1287/mnsc.24.5.485
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    Cited by:

    1. Kimms, A, 1998. "Stability Measures for Rolling Schedules with Applications to Capacity Expansion Planning, Master Production Scheduling, and Lot Sizing," Omega, Elsevier, vol. 26(3), pages 355-366, June.
    2. King, Robert P., 1979. "Operational Techniques for Applied Decision Analysis Under Uncertainty," AAEA Fellows - Dissertations and Theses, Agricultural and Applied Economics Association, number 181951, December.
    3. Balakrishnan, Jaydeep & Hung Cheng, Chun, 2009. "The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty," Omega, Elsevier, vol. 37(1), pages 165-177, February.
    4. Kimms, Alf, 1996. "Stability measures for rolling schedules with applications to capacity expansion planning, master production scheduling, and lot sizing," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 418, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    5. Torpong Cheevaprawatdomrong & Robert L. Smith, 2004. "Infinite Horizon Production Scheduling in Time-Varying Systems Under Stochastic Demand," Operations Research, INFORMS, vol. 52(1), pages 105-115, February.
    6. G. Rius-Sorolla & J. Maheut & S. Estellés-Miguel & J. P. García-Sabater, 2021. "Operations planning test bed under rolling horizons, multiproduct, multiechelon, multiprocess for capacitated production planning modelling with strokes," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1289-1315, December.
    7. Drouin, Nicol & Gautier, Antoine & Lamond, Bernard F. & Lang, Pascal, 1996. "Piecewise affine approximations for the control of a one-reservoir hydroelectric system," European Journal of Operational Research, Elsevier, vol. 89(1), pages 53-69, February.
    8. Suresh Chand & Vernon Ning Hsu & Suresh Sethi, 2002. "Forecast, Solution, and Rolling Horizons in Operations Management Problems: A Classified Bibliography," Manufacturing & Service Operations Management, INFORMS, vol. 4(1), pages 25-43, September.
    9. Zhaotong Lian & Liming Liu & Stuart X. Zhu, 2010. "Rolling‐horizon replenishment: Policies and performance analysis," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(6), pages 489-502, September.

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