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A decision making framework in production processes using Bayesian inference and stochastic dynamic programming

Akhavan Niaki, T ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.3923/jas.2007.3618.3627
  3. Publisher: Asian Network for Scientific Information , 2007
  4. Abstract:
  5. In order to design a decision-making framework in production environments, in this study, we use both the stochastic dynamic programming and Bayesian inference concepts. Using the posterior probability of the production process to be in state λ (the hazard rate of defective products), first we formulate the problem into a stochastic dynamic programming model. Next, we derive some properties for the optimal value of the objective function. Then, we propose a solution algorithm. At the end, the applications and the performances of the proposed methodology are demonstrated by two numerical examples. © 2007 Asian Network for Scientific Information
  6. Keywords:
  7. Bayesian networks ; Decision making ; Inference engines ; Stochastic models ; Stochastic systems ; Bayesian inference ; Decision-making frameworks ; Gamma distribution ; Posterior probability ; Production environments ; Production process ; Stochastic dynamic programming ; Stochastic dynamic programming model ; Dynamic programming
  8. Source: Journal of Applied Sciences ; Volume 7, Issue 23 , 2007 , Pages 3618-3627 ; 18125654 (ISSN)
  9. URL: https://scialert.net/abstract/?doi=jas.2007.3618.3627