Controlled Markov chains with exponential risk-sensitive criteria: Modularity, structured policies and applications
| dc.contributor.advisor | Greenlee, Wilfred M. | en_US |
| dc.contributor.author | Avila Godoy, Micaela Guadalupe | |
| dc.creator | Avila Godoy, Micaela Guadalupe | en_US |
| dc.date.accessioned | 2013-05-09T09:27:37Z | |
| dc.date.available | 2013-05-09T09:27:37Z | |
| dc.date.issued | 1999 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/289049 | |
| dc.description.abstract | Controlled Markov chains (CMC's) are mathematical models for the control of sequential decision stochastic systems. Starting in the early 1950's with the work of R. Bellman, many basic contributions to CMC's have been made, and numerous applications to engineering, operation research, and economics, among other areas, have been developed. The optimal control problem for CMC's with a countable state space, and with a general action space, is studied for (exponential) total and discounted risk-sensitive cost criteria. General (dynamic programming) results for the finite and the infinite horizon cases are obtained. A set of general conditions is presented to obtain structural properties of the optimal value function and policies. In particular, monotonicity properties of value functions and optimal policies are established. The approach followed is to show the (sub)modularity of certain functions (related to the optimality equations). Four applications studies are used to illustrate the general results obtained is this dissertation: equipment replacement, optimal resource allocation, scheduling of uncertain jobs, and inventory control. | |
| dc.language.iso | en_US | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. | en_US |
| dc.subject | Mathematics. | en_US |
| dc.title | Controlled Markov chains with exponential risk-sensitive criteria: Modularity, structured policies and applications | en_US |
| dc.type | text | en_US |
| dc.type | Dissertation-Reproduction (electronic) | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | doctoral | en_US |
| dc.identifier.proquest | 9957946 | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.discipline | Mathematics | en_US |
| thesis.degree.name | Ph.D. | en_US |
| dc.identifier.bibrecord | .b40137600 | en_US |
| refterms.dateFOA | 2018-08-29T06:20:01Z | |
| html.description.abstract | Controlled Markov chains (CMC's) are mathematical models for the control of sequential decision stochastic systems. Starting in the early 1950's with the work of R. Bellman, many basic contributions to CMC's have been made, and numerous applications to engineering, operation research, and economics, among other areas, have been developed. The optimal control problem for CMC's with a countable state space, and with a general action space, is studied for (exponential) total and discounted risk-sensitive cost criteria. General (dynamic programming) results for the finite and the infinite horizon cases are obtained. A set of general conditions is presented to obtain structural properties of the optimal value function and policies. In particular, monotonicity properties of value functions and optimal policies are established. The approach followed is to show the (sub)modularity of certain functions (related to the optimality equations). Four applications studies are used to illustrate the general results obtained is this dissertation: equipment replacement, optimal resource allocation, scheduling of uncertain jobs, and inventory control. |
