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Horizon Effects in Aggregate Production Planning with Seasonal Demand

Author

Listed:
  • John O. McClain

    (Cornell University)

  • Joseph Thomas

    (Cornell University)

Abstract

When a demand pattern is dominated by a seasonal effect, the concept of a steady state solution can be used in two ways in aggregate production planning. First, general policy recommendations can be made concerning the use of seasonal workforce changes versus overtime and seasonal inventory. Second, the results can be used to provide ending conditions in an intermediate range planning algorithm with a moving horizon. These ideas are explored using a linear programming model, making use of known planning horizon properties. A simulation experiment tests the efficacy of short horizon deterministic models in a stochastic environment, and demonstrates that ending conditions, derived from the steady state model, improve decisions under a variety of conditions on costs and horizon length.

Suggested Citation

  • John O. McClain & Joseph Thomas, 1977. "Horizon Effects in Aggregate Production Planning with Seasonal Demand," Management Science, INFORMS, vol. 23(7), pages 728-736, March.
    • Handle: RePEc:inm:ormnsc:v:23:y:1977:i:7:p:728-736
    DOI: 10.1287/mnsc.23.7.728
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    Citations

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    Cited by:

    1. Dobos, Imre, 1996. "Aggregate planning with continuous time," International Journal of Production Economics, Elsevier, vol. 43(1), pages 1-9, May.
    2. Mariel Lavieri & Martin Puterman, 2009. "Optimizing nursing human resource planning in British Columbia," Health Care Management Science, Springer, vol. 12(2), pages 119-128, June.
    3. Scott A. Neslin & Thomas P. Novak & Kenneth R. Baker & Donna L. Hoffman, 2009. "An Optimal Contact Model for Maximizing Online Panel Response Rates," Management Science, INFORMS, vol. 55(5), pages 727-737, May.
    4. Hartmut Stadtler, 2000. "Improved Rolling Schedules for the Dynamic Single-Level Lot-Sizing Problem," Management Science, INFORMS, vol. 46(2), pages 318-326, February.
    5. Lejeune, M.A., 2006. "A variable neighborhood decomposition search method for supply chain management planning problems," European Journal of Operational Research, Elsevier, vol. 175(2), pages 959-976, December.
    6. Leif K Sandal & Sturla F Kvamsdal & José M Maroto & Manuel Morán, 2021. "A contraction approach to dynamic optimization problems," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    7. Molinder, Anders, 1995. "Application of calculus of variations to a continuous time aggregate production model," International Journal of Production Economics, Elsevier, vol. 41(1-3), pages 273-280, October.
    8. Kvamsdal, Sturla F. & Maroto, José M. & Morán, Manuel & Sandal, Leif K., 2020. "Bioeconomic modeling of seasonal fisheries," European Journal of Operational Research, Elsevier, vol. 281(2), pages 332-340.
    9. Nagaraja, C.H. & Thavaneswaran, A. & Appadoo, S.S., 2015. "Measuring the bullwhip effect for supply chains with seasonal demand components," European Journal of Operational Research, Elsevier, vol. 242(2), pages 445-454.
    10. Nicholas G. Hall & Marc E. Posner & Chris N. Potts, 2021. "Online production planning to maximize the number of on-time orders," Annals of Operations Research, Springer, vol. 298(1), pages 249-269, March.
    11. 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.

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