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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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    2. Serhat Gul & Brian T. Denton & John W. Fowler, 2015. "A Progressive Hedging Approach for Surgery Planning Under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 755-772, November.
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    5. Tsay, Calvin, 2024. "A Quantile Neural Network Framework for Twostage Stochastic Optimization," DES - Working Papers. Statistics and Econometrics. WS 43773, Universidad Carlos III de Madrid. Departamento de Estadística.
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    8. Badri, Hossein & Fatemi Ghomi, S.M.T. & Hejazi, Taha-Hossein, 2017. "A two-stage stochastic programming approach for value-based closed-loop supply chain network design," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 1-17.

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