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Stochastic Optimization of Production Planning

J. H. Beebe, Charles S. Beightler, J. P. Stark

Open publisher page 8 citations

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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What this paper is about

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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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Markov decision process, Stochastic programming, Mathematical optimization, Production planning, Computer science, Production (economics), Markov process, Dynamic programming

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