Reward model solution methods with impulse and rate rewards: An algorithm and numerical results
| dc.contributor.advisor | Sanders, William H. | en_US |
| dc.contributor.author | Qureshi, Muhammad Akber, 1964- | |
| dc.creator | Qureshi, Muhammad Akber, 1964- | en_US |
| dc.date.accessioned | 2013-04-03T13:15:42Z | |
| dc.date.available | 2013-04-03T13:15:42Z | |
| dc.date.issued | 1992 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/278184 | |
| dc.description.abstract | Reward models have become an important method for specifying performability models for many types of systems. Many methods have been proposed for solving these reward models, but no method has proven itself to be applicable over all system classes and sizes. Furthermore, specification of reward models has usually been done at the state level, which can be extremely cumbersome for realistic models. We describe a method to specify reward models as stochastic activity networks (SANs) with impulse and rate rewards, and a method by which to solve these models via randomization. The method is an extension of one proposed by de Souza e Silva and Gail in which impulse and rate rewards are specified at the SAN level, and solved in a single model. Furthermore, a novel method of discarding trajectories of low probabilities with algorithms to compute bounds on the injected error is proposed. The methodology is presented, together with the results on the time and space efficiency of a particular implementation. | |
| 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 | Engineering, Electronics and Electrical. | en_US |
| dc.subject | Computer Science. | en_US |
| dc.title | Reward model solution methods with impulse and rate rewards: An algorithm and numerical results | en_US |
| dc.type | text | en_US |
| dc.type | Thesis-Reproduction (electronic) | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | masters | en_US |
| dc.identifier.proquest | 1349471 | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.name | M.S. | en_US |
| dc.identifier.bibrecord | .b27698841 | en_US |
| refterms.dateFOA | 2018-04-26T13:24:40Z | |
| html.description.abstract | Reward models have become an important method for specifying performability models for many types of systems. Many methods have been proposed for solving these reward models, but no method has proven itself to be applicable over all system classes and sizes. Furthermore, specification of reward models has usually been done at the state level, which can be extremely cumbersome for realistic models. We describe a method to specify reward models as stochastic activity networks (SANs) with impulse and rate rewards, and a method by which to solve these models via randomization. The method is an extension of one proposed by de Souza e Silva and Gail in which impulse and rate rewards are specified at the SAN level, and solved in a single model. Furthermore, a novel method of discarding trajectories of low probabilities with algorithms to compute bounds on the injected error is proposed. The methodology is presented, together with the results on the time and space efficiency of a particular implementation. |
