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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Vydáno v:European journal of operational research Ročník 213; číslo 1; s. 166 - 179
Hlavní autoři: Körpeoğlu, Ersin, Yaman, Hande, Selim Aktürk, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 16.08.2011
Elsevier
Elsevier Sequoia S.A
Edice:European Journal of Operational Research
Témata:
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
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
ISSN:0377-2217, 1872-6860
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Popis
Shrnutí: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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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2011.02.032