Stochastic Optimization of Production Planning
J. H. Beebe, Charles S. Beightler, J. P. Stark
Abstract
J. H. Beebe, Charles S. Beightler, J. P. Stark
Abstract
A multistage decision problem is optimized using a new formulation of stochastic dynamic programming. The problem optimized in this paper concerns a semiconductor production process where the transitions at each work station are stochastic. The mathematical model employs at one stage a Markov decision process with an infinite number of substages and shows how this process may be compressed and handled as one stage in the larger problem.
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A multistage decision problem is optimized using a new formulation of stochastic dynamic programming. The problem optimized in this paper concerns a semiconductor production process where the transitions at each work station are stochastic. The mathematical model employs at one stage a Markov decision process with an infinite number of substages and shows how this process may be compressed and handled as one stage in the larger problem.
Key concepts: Markov decision process, Stochastic programming, Mathematical optimization, Production planning, Computer science, Production (economics), Markov process, Dynamic programming