A multi-stage stochastic programming approach in master production scheduling
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 assu...
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| Vydané v: | European journal of operational research Ročník 213; číslo 1; s. 166 - 179 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
16.08.2011
Elsevier Elsevier Sequoia S.A |
| Edícia: | European Journal of Operational Research |
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| ISSN: | 0377-2217, 1872-6860 |
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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. |
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| AbstractList | 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. 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. [PUBLICATION ABSTRACT] |
| Author | Yaman, Hande Körpeoğlu, Ersin Selim Aktürk, M. |
| Author_xml | – sequence: 1 givenname: Ersin surname: Körpeoğlu fullname: Körpeoğlu, Ersin email: ekorpeog@andrew.cmu.edu organization: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA – sequence: 2 givenname: Hande surname: Yaman fullname: Yaman, Hande email: hyaman@bilkent.edu.tr organization: Department of Industrial Engineering, Bilkent University, 06800 Ankara, Turkey – sequence: 3 givenname: M. surname: Selim Aktürk fullname: Selim Aktürk, M. email: akturk@bilkent.edu.tr organization: Department of Industrial Engineering, Bilkent University, 06800 Ankara, Turkey |
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| Keywords | Master production scheduling Controllable processing times Stochastic programming Flexible manufacturing Script Processing time Master production schedule Stochastic method Integrated management Production management Flexible manufacturing system Heuristic method Controllability Profit Capacity constraint Firm management Production capacity Resource management Execution time |
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| SubjectTerms | Applied sciences Computation Computer science; control theory; systems Control system analysis Control theory. Systems Controllable processing times Demand Enterprise resource planning Exact sciences and technology Firm modelling Flexible manufacturing Heuristic Inventory control, production control. Distribution Marketing Master production scheduling Mathematical programming Operational research Operational research and scientific management Operational research. Management science Production scheduling Programming Stability Stochastic models Stochastic programming Stochastic programming Master production scheduling Flexible manufacturing Controllable processing times Stochasticity Studies |
| Title | A multi-stage stochastic programming approach in master production scheduling |
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