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A master production scheduling procedure for stochastic demand and rolling planning horizons


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  • Vargas, Vicente
  • Metters, Richard
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    The problem of interest is a one product, uncapacitated master production schedule (MPS) in which decisions are made under rolling planning horizons. Demand is stochastic and time varying, and effectiveness is measured by inventory holding, production setup, and backorder costs. Typically, in both the research literature and the business practice the stochastic nature of the problem is modeled in an ad hoc fashion. The stochastic MPS problem is usually solved by adding safety stock to production quantities obtained from a deterministic lot-sizing algorithm. Here, the stochastic nature of the problem is explicitly considered, as an optimal algorithm for solving the static probabilistic dynamic lot-sizing problem is adapted to rolling planning horizons. The resulting algorithm is found to dominate traditional approaches over a wide variety of experimental factors, reducing total costs by an average of 16% over traditional methods.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 132 (2011)
    Issue (Month): 2 (August)
    Pages: 296-302

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    Handle: RePEc:eee:proeco:v:132:y:2011:i:2:p:296-302

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    Keywords: Inventory management Master production scheduling Simulation;


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    1. Harvey M. Wagner & Thomson M. Whitin, 1958. "Dynamic Version of the Economic Lot Size Model," Management Science, INFORMS, vol. 5(1), pages 89-96, October.
    2. Richard Ehrhardt, 1979. "The Power Approximation for Computing (s, S) Inventory Policies," Management Science, INFORMS, vol. 25(8), pages 777-786, August.
    3. 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.
    4. Vargas, Vicente, 2009. "An optimal solution for the stochastic version of the Wagner-Whitin dynamic lot-size model," European Journal of Operational Research, Elsevier, vol. 198(2), pages 447-451, October.
    5. Rezaei, Jafar & Davoodi, Mansoor, 2011. "Multi-objective models for lot-sizing with supplier selection," International Journal of Production Economics, Elsevier, vol. 130(1), pages 77-86, March.
    6. James H. Bookbinder & Jin-Yan Tan, 1988. "Strategies for the Probabilistic Lot-Sizing Problem with Service-Level Constraints," Management Science, INFORMS, vol. 34(9), pages 1096-1108, September.
    7. Samuel Karlin, 1960. "Dynamic Inventory Policy with Varying Stochastic Demands," Management Science, INFORMS, vol. 6(3), pages 231-258, April.
    8. Liao, Gwo-Liang & Sheu, Shey-Huei, 2011. "Economic production quantity model for randomly failing production process with minimal repair and imperfect maintenance," International Journal of Production Economics, Elsevier, vol. 130(1), pages 118-124, March.
    9. Cardós, Manuel & Babiloni, Eugenia, 2011. "Exact and approximate calculation of the cycle service level in periodic review inventory policies," International Journal of Production Economics, Elsevier, vol. 131(1), pages 63-68, May.
    10. Dawande, Milind & Gavirneni, Srinagesh & Naranpanawe, Sanjeewa & Sethi, Suresh P., 2009. "Discrete forecast horizons for two-product variants of the dynamic lot-size problem," International Journal of Production Economics, Elsevier, vol. 120(2), pages 430-436, August.
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    Cited by:
    1. Lee, Shine-Der & Lan, Shu-Chuan, 2013. "Production lot sizing with a secondary outsourcing facility," International Journal of Production Economics, Elsevier, vol. 141(1), pages 414-424.
    2. Gahm, Christian & Dünnwald, Bastian & Sahamie, Ramin, 2014. "A multi-criteria master production scheduling approach for special purpose machinery," International Journal of Production Economics, Elsevier, vol. 149(C), pages 89-101.


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