| LDR | | 02768nmm uu200469 4500 |
| 001 | | 000000332996 |
| 005 | | 20240805172006 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438072909 |
| 035 | |
▼a (MiAaPQ)AAI10792872 |
| 035 | |
▼a (MiAaPQ)iastate:17362 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 658 |
| 100 | 1 |
▼a Guo, Ge.
▼0 (orcid)0000-0001-8404-2256 |
| 245 | 10 |
▼a Solution Methods and Bounds for Two-stage Risk-neutral and Multistage Risk-averse Stochastic Mixed-integer Programs with Applications in Energy and Manufacturing. |
| 260 | |
▼a [S.l.] :
▼b Iowa State University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 113 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Adviser: Sarah M. Ryan. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Iowa State University, 2018. |
| 520 | |
▼a This dissertation presents an integrated method for solving stochastic mixed-integer programs, develops a lower bounding approach for multistage risk-averse stochastic mixed-integer programs, and proposes an optimization formulation for mixed-mo |
| 520 | |
▼a It is well known that a stochastic mixed-integer program is difficult to solve due to its non-convexity and stochastic factors. The scenario decomposition algorithms display computational advantage when dealing with a large number of possible re |
| 520 | |
▼a In many applications, the decision makers are risk-averse and are more concerned with large losses in the worst scenarios than with average performance. The PH algorithm can serve as a time-efficient heuristic for risk-averse stochastic mixed-in |
| 520 | |
▼a The existing optimization formulations for MMALS problems do not consider many real-world uncertainty factors such as timely part delivery and material quality. In addition, real-time sequencing decisions are required to deal with inevitable dis |
| 520 | |
▼a Computational studies show that the integration of PH helps DD to reduce the run-time significantly, and the lower bounding approach can obtain convergent and tight lower bounds to help PH evaluate quality of solutions. The PH algorithm and the |
| 590 | |
▼a School code: 0097. |
| 650 | 4 |
▼a Industrial engineering. |
| 650 | 4 |
▼a Operations research. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0546 |
| 690 | |
▼a 0796 |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Iowa State University.
▼b Industrial and Manufacturing Systems Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0097 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997728
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |