Stochastic linear programming to optimize some stochastic systems

G. Pérez-Lechuga, M. M. Álvarez-Suárez, J. Garnica-González, H. Niccolas-Morales, F. Venegas-Martínez

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this document we propose a discrete time Markov decision process with finite state to represent some stochastic and dynamical systems. Our problem consists on finding the optimal policy that maximizes the expected average reward per unit of time under an infinite planning horizon using stochastic linear programming. We analyze the feasibility and optimality properties of the model allowing that some of the elements of the A matrix of technological coefficients to be random. Our aim is to enable the transition probability matrix thinking of substituting it punctual values by some probability density functions. We report the theoretical results and some numeric examples.

Original languageEnglish
Pages (from-to)2263-2268
Number of pages6
JournalWSEAS Transactions on Systems
Volume5
Issue number9
StatePublished - Sep 2006
Externally publishedYes

Keywords

  • Discrete dynamical systems
  • Feasibility solutions
  • Markov chains
  • Random search methods
  • Stochastic linear programming

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