Markov-renewal programming under incomplete information

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dc.creatorPrasad, Janniah S.
dc.date.available2011-02-18T20:56:52Z
dc.date.issued1971-08
dc.degree.departmentIndustrial and Systems Engineeringen_US
dc.description.abstractScientific decision making involves model building for operational systems and solving them to determine the optimal or an acceptable decision. Generally, as the mathematical model gets closer to the true representation of the system, its degree of complexity increases, rendering solution of the model very difficult. The solution of most practical problems is therefore the outgrowth of several prior developments involving simplified problems. This has been the case in the development of the theory of sequential decision making also. Since the development of dynamic programming, it has grown from simple models with limited practical utility to more complex models with correspondingly wider applicability.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/2346/15103en_US
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.rights.availabilityUnrestricted.
dc.subjectMarkov processesen_US
dc.subjectDecision makingen_US
dc.titleMarkov-renewal programming under incomplete information
dc.typeDissertation
thesis.degree.departmentIndustrial and Systems Engineering
thesis.degree.departmentIndustrial Engineering
thesis.degree.disciplineIndustrial and Systems Engineering
thesis.degree.grantorTexas Tech University
thesis.degree.levelDoctoral
thesis.degree.namePh.D.

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