| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000331188 |
| 005 | | 20241112174749 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438342385 |
| 035 | |
▼a (MiAaPQ)AAI10839043 |
| 035 | |
▼a (MiAaPQ)cornellgrad:10925 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 658 |
| 100 | 1 |
▼a Girard, Cory Jay. |
| 245 | 10 |
▼a Structural Results for Constrained Markov Decision Processes. |
| 260 | |
▼a [S.l.] :
▼b Cornell University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 159 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Mark E. Lewis. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a In the existing literature on the dynamic control of service systems, a decision-maker seeks to optimize a single performance metric over a given time-horizon. However, in many settings, the decision-maker may be interested in multiple performan |
| 520 | |
▼a We formulate this problem (the parallel setting), as well as a related problem in which customers undergo two phases of service in series (the tandem setting), as Constrained Markov Decision Processes (CMDPs). We present a general framework for |
| 520 | |
▼a Lastly, we consider a controlled, truncated birth-death chain motivated by optimal treatment prescription in the context of personalized medicine. In this model, states represent the patient's state of health, and treatments can be prescribed to |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Operations research. |
| 690 | |
▼a 0796 |
| 710 | 20 |
▼a Cornell University.
▼b Operations Research. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0058 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999668
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |