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Freezing the Master Production Schedule Under Rolling Planning Horizons

Author

Listed:
  • V. Sridharan

    (Department of Management, Clemson University, Clemson, South Carolina 29634)

  • William L. Berry

    (College of Business Administration, University of Iowa, Iowa City, Iowa 52242)

  • V. Udayabhanu

    (School of Business, San Francisco State University, San Francisco, California 94132)

Abstract

The stability of the Master Production Schedule (MPS) is a critical issue in managing production operations with a Material Requirements Planning System. One method of achieving stability is to freeze some portion or all of the MPS. While freezing the MPS can limit the number of schedule changes, it can also produce an increase in production and inventory costs. This paper examines three decision variables in freezing the MPS: the freezing method, the freeze interval length, and the planning horizon length. Simulation experiment results are reported which suggest that freezing up to 50% of the planning horizon has a marginal effect on production and inventory cost under a wide range of operating conditions. These results also suggest that an order based freezing method produces superior results in comparison with a period based method.

Suggested Citation

  • V. Sridharan & William L. Berry & V. Udayabhanu, 1987. "Freezing the Master Production Schedule Under Rolling Planning Horizons," Management Science, INFORMS, vol. 33(9), pages 1137-1149, September.
    • Handle: RePEc:inm:ormnsc:v:33:y:1987:i:9:p:1137-1149
    DOI: 10.1287/mnsc.33.9.1137
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    Citations

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    Cited by:

    1. 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.
    2. Thomas W.M. Vossen & Fan You & Dan Zhang, 2022. "Finite‐horizon approximate linear programs for capacity allocation over a rolling horizon," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2127-2142, May.
    3. 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.
    4. Tang, Ou & Grubbstrom, Robert W., 2002. "Planning and replanning the master production schedule under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 78(3), pages 323-334, August.
    5. Peidro, David & Mula, Josefa & Jiménez, Mariano & del Mar Botella, Ma, 2010. "A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment," European Journal of Operational Research, Elsevier, vol. 205(1), pages 65-80, August.
    6. Schneeweiss, Ch. & Schneider, H., 1999. "Measuring and designing flexibility as a generalized service degree," European Journal of Operational Research, Elsevier, vol. 112(1), pages 98-106, January.
    7. Vargas, Vicente & Metters, Richard, 2011. "A master production scheduling procedure for stochastic demand and rolling planning horizons," International Journal of Production Economics, Elsevier, vol. 132(2), pages 296-302, August.
    8. Feng Li & Zhou Xu & Zhi-Long Chen, 2020. "Production and Transportation Integration for Commit-to-Delivery Mode with General Shipping Costs," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1012-1029, October.
    9. Sahin, Funda & Powell Robinson, E. & Gao, Li-Lian, 2008. "Master production scheduling policy and rolling schedules in a two-stage make-to-order supply chain," International Journal of Production Economics, Elsevier, vol. 115(2), pages 528-541, October.
    10. Meixell, Mary J., 2005. "The impact of setup costs, commonality, and capacity on schedule stability: An exploratory study," International Journal of Production Economics, Elsevier, vol. 95(1), pages 95-107, January.
    11. Laurent Lim, Lâm & Alpan, Gülgün & Penz, Bernard, 2014. "Reconciling sales and operations management with distant suppliers in the automotive industry: A simulation approach," International Journal of Production Economics, Elsevier, vol. 151(C), pages 20-36.
    12. 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.
    13. Segerstedt, Anders, 1996. "Formulas of MRP," International Journal of Production Economics, Elsevier, vol. 46(1), pages 127-136, December.
    14. Slama, Ilhem & Ben-Ammar, Oussama & Thevenin, Simon & Dolgui, Alexandre & Masmoudi, Faouzi, 2022. "Stochastic program for disassembly lot-sizing under uncertain component refurbishing lead times," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1183-1198.
    15. Zhao, Xiande & Lam, Kokin, 1997. "Lot-sizing rules and freezing the master production schedule in material requirements planning systems," International Journal of Production Economics, Elsevier, vol. 53(3), pages 281-305, December.
    16. Demirel, Edil & Özelkan, Ertunga C. & Lim, Churlzu, 2018. "Aggregate planning with Flexibility Requirements Profile," International Journal of Production Economics, Elsevier, vol. 202(C), pages 45-58.
    17. Xie, Jinxing & Zhao, Xiande & Lee, T. S., 2003. "Freezing the master production schedule under single resource constraint and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 83(1), pages 65-84, January.
    18. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).
    19. Körpeoglu, Ersin & Yaman, Hande & Selim Aktürk, M., 2011. "A multi-stage stochastic programming approach in master production scheduling," European Journal of Operational Research, Elsevier, vol. 213(1), pages 166-179, August.
    20. Mula, Josefa & Peidro, David & Poler, Raul, 2010. "The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand," International Journal of Production Economics, Elsevier, vol. 128(1), pages 136-143, November.

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