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      A multi-stage stochastic programming approach in master production scheduling

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      Author
      Körpeoğlu, E.
      Yaman, H.
      Aktürk, M. S.
      Date
      2011
      Source Title
      European Journal of Operational Research
      Print ISSN
      0377-2217
      Electronic ISSN
      1872-6860
      Publisher
      Elsevier
      Volume
      213
      Issue
      1
      Pages
      166 - 179
      Language
      English
      Type
      Article
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      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.
      Keywords
      Controllable processing times
      Flexible manufacturing
      Master production scheduling
      Stochastic programming
      Permalink
      http://hdl.handle.net/11693/21821
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.ejor.2011.02.032
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