東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Water Network Design and Management ...

Zhang, Weini.

FindBook

Google Book

Amazon

博客來

Water Network Design and Management via Stochastic Programming.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Water Network Design and Management via Stochastic Programming./
作者:	Zhang, Weini.
面頁冊數:	161 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
Contained By:	Dissertation Abstracts International75-04B(E).
標題:	Natural Resource Management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3605094
ISBN:	9781303611247

Water Network Design and Management via Stochastic Programming.
Zhang, Weini.

Water Network Design and Management via Stochastic Programming. - 161 p.

Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.

Thesis (Ph.D.)--The University of Arizona, 2013.

This item must not be sold to any third party vendors.

Water is an essential natural resource for life and economic activities. Water resources management is facing major challenges due to increasing demands caused by population growth, increased industrial and agricultural use, and depletion of fresh water sources around the world. In addition to putting stress on our civilization, factors such as water supply availability, spatial population changes, industrial growth, etc. are all sources of major uncertainty in water resources management. There are also uncertainties regarding climate variability and how it affects both water demands and supplies. Stochastic programming is a mathematical tool to help make decisions under uncertainty that models the uncertain parameters using probability distributions and incorporates probabilistic statements in mathematical optimization. This dissertation applies stochastic programming to water resources management. In particular, we focus on reclaimed water distribution network design to effectively reuse water in a municipal system and a water allocation problem in an integrated water system under uncertainty.

ISBN: 9781303611247Subjects--Topical Terms:

676989
Natural Resource Management.

Water Network Design and Management via Stochastic Programming.
LDR:03657nmm a2200313 4500 001 2056983
005 20150630121433.5
008 170521s2013 ||||||||||||||||| ||eng d
020 $a 9781303611247
035 $a (MiAaPQ)AAI3605094
035 $a AAI3605094
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Weini. $3 3170782
245 1 0 $a Water Network Design and Management via Stochastic Programming.
300 $a 161 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-04(E), Section: B.
500 $a Adviser: Guzin Bayraksan.
502 $a Thesis (Ph.D.)--The University of Arizona, 2013.
506 $a This item must not be sold to any third party vendors.
520 $a Water is an essential natural resource for life and economic activities. Water resources management is facing major challenges due to increasing demands caused by population growth, increased industrial and agricultural use, and depletion of fresh water sources around the world. In addition to putting stress on our civilization, factors such as water supply availability, spatial population changes, industrial growth, etc. are all sources of major uncertainty in water resources management. There are also uncertainties regarding climate variability and how it affects both water demands and supplies. Stochastic programming is a mathematical tool to help make decisions under uncertainty that models the uncertain parameters using probability distributions and incorporates probabilistic statements in mathematical optimization. This dissertation applies stochastic programming to water resources management. In particular, we focus on reclaimed water distribution network design to effectively reuse water in a municipal system and a water allocation problem in an integrated water system under uncertainty.
520 $a We first present a two-stage stochastic integer program with recourse for cost-effective reclaimed water network design. Unlike other formulations, uncertain demands, temporal, and spatial population changes are explicitly considered in our model. Selection of pipe and pump sizes are modeled using binary variables in order to linearize the nonlinear hydraulic equations and objective function terms. We then develop preprocessing methods to significantly reduce the problem dimension by exploiting the problem characteristics and network structure. We analyze the sensitivity of the network design under varying model parameters, present computational results, and discuss when the stochastic solution is most valuable.
520 $a Next, we investigate the use of risk-averse approach in water resources management using the so-called conditional value-at-risk as a risk measure. We develop a multistage risk-averse stochastic program with recourse for long-term water allocation under uncertain demands and water supply variability. We propose a specialized decomposition-based algorithm to solve multistage risk-averse stochastic programs, and present both the single-cut and the multicut version of the algorithm. We then compare the solution methodologies with different ways of decomposing the resulting problem. We solve the multistage risk-averse water allocation problem with different risk aversion levels and model assumptions, present computational results to demonstrate the potential benefits of risk-averse approach, and provide a guideline for risk aversion level selection.
590 $a School code: 0009.
650 4 $a Natural Resource Management. $3 676989
650 4 $a Engineering, Industrial. $3 626639
690 $a 0528
690 $a 0546
710 2 $a The University of Arizona. $b Systems & Industrial Engineering. $3 1278304
773 0 $t Dissertation Abstracts International $g 75-04B(E).
790 $a 0009
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3605094