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A multi-stage stochastic programming approach in master production scheduling

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  • Körpeoglu, Ersin
  • Yaman, Hande
  • Selim Aktürk, M.

Abstract

Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times, and a single scenario for demand forecasts. In this paper, we question these assumptions and consider a problem with finite capacity, controllable processing times, and several demand scenarios instead of just one. We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios. Controllable processing times enlarge the solution space so that the limited capacity of production resources are utilized more effectively. We propose an effective formulation that enables an extensive computational study. Our computational results clearly indicate that instead of relying on relatively simple heuristic methods, multi-stage stochastic programming can be used effectively to solve MPS problems, and that controllability increases the performance of multi-stage solutions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:213:y:2011:i:1:p:166-179
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    1. 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.
    2. Cheng, T.C. Edwin & Kovalyov, Mikhail Y. & Shakhlevich, Natalia V., 2006. "Scheduling with controllable release dates and processing times: Total completion time minimization," European Journal of Operational Research, Elsevier, vol. 175(2), pages 769-781, December.
    3. Charles C. Holt & Franco Modigliani & John F. Muth, 1956. "Derivation of a Linear Decision Rule for Production and Employment," Management Science, INFORMS, vol. 2(2), pages 159-177, January.
    4. Guilherme Emani Vieira, 2006. "A Practical View of the Complexity in Developing Master Production Schedules: Fundamentals, Examples, and Implementation," International Series in Operations Research & Management Science, in: Jeffrey W. Herrmann (ed.), Handbook of Production Scheduling, chapter 0, pages 149-176, Springer.
    5. 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.
    6. Leyvand, Yaron & Shabtay, Dvir & Steiner, George, 2010. "A unified approach for scheduling with convex resource consumption functions using positional penalties," European Journal of Operational Research, Elsevier, vol. 206(2), pages 301-312, October.
    7. Balibek, Emre & Köksalan, Murat, 2010. "A multi-objective multi-period stochastic programming model for public debt management," European Journal of Operational Research, Elsevier, vol. 205(1), pages 205-217, August.
    8. Edward H. Bowman, 1956. "Production Scheduling by the Transportation Method of Linear Programming," Operations Research, INFORMS, vol. 4(1), pages 100-103, February.
    9. Gurel, Sinan & Akturk, M. Selim, 2007. "Optimal allocation and processing time decisions on non-identical parallel CNC machines: [epsilon]-constraint approach," European Journal of Operational Research, Elsevier, vol. 183(2), pages 591-607, December.
    10. S Karabuk, 2008. "Production planning under uncertainty in textile manufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 510-520, April.
    11. 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.
    12. Stefan Voß & David L. Woodruff, 2006. "Introduction to Computational Optimization Models for Production Planning in a Supply Chain," Springer Books, Springer, edition 0, number 978-3-540-29879-3, January.
    13. A. Charnes & W. W. Cooper & G. H. Symonds, 1958. "Cost Horizons and Certainty Equivalents: An Approach to Stochastic Programming of Heating Oil," Management Science, INFORMS, vol. 4(3), pages 235-263, April.
    14. Alper Atamtürk & Muhong Zhang, 2007. "Two-Stage Robust Network Flow and Design Under Demand Uncertainty," Operations Research, INFORMS, vol. 55(4), pages 662-673, August.
    15. Kai Huang & Shabbir Ahmed, 2009. "The Value of Multistage Stochastic Programming in Capacity Planning Under Uncertainty," Operations Research, INFORMS, vol. 57(4), pages 893-904, August.
    16. Kayan, Rabia K. & Akturk, M. Selim, 2005. "A new bounding mechanism for the CNC machine scheduling problems with controllable processing times," European Journal of Operational Research, Elsevier, vol. 167(3), pages 624-643, December.
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    6. Liu, Yu & Zhang, Qin & Ouyang, Zhiyuan & Huang, Hong-Zhong, 2021. "Integrated production planning and preventive maintenance scheduling for synchronized parallel machines," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
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