ICA  Vol.3 No.4 , November 2012
A Reward Functional to Solve the Replacement Problem
The replacement problem can be modeled as a finite, irreducible, homogeneous Markov Chain. In our proposal the problem was modeled using a Markov decision process and then, the instance was optimized using dynamic programming. We proposed a new functional that includes a reward functional, that can be more helpful in processing industries because it considerate instances like incomes, maintenance costs, fixed costs to replace equipment, purchase price and salvage values; and this functional can be solved with dynamic programming and used to make effective decisions. Two theorems are proved related with this new functional. A numerical example is presented in order to demonstrate the utility of this proposal.

Cite this paper
E. Gress, O. Arango, J. Armenta and A. Reyes, "A Reward Functional to Solve the Replacement Problem," Intelligent Control and Automation, Vol. 3 No. 4, 2012, pp. 413-418. doi: 10.4236/ica.2012.34045.
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