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

Stochastic models for capacity manag...

Wang, Lei.

FindBook

Google Book

Amazon

博客來

Stochastic models for capacity management in presence of time-differentiated customer classes.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Stochastic models for capacity management in presence of time-differentiated customer classes./
作者:	Wang, Lei.
面頁冊數:	168 p.
附註:	Adviser: Diwakar Gupta.
Contained By:	Dissertation Abstracts International67-06B.
標題:	Engineering, Industrial. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3220046
ISBN:	9780542735493

Stochastic models for capacity management in presence of time-differentiated customer classes.
Wang, Lei.

Stochastic models for capacity management in presence of time-differentiated customer classes. - 168 p.

Adviser: Diwakar Gupta.

Thesis (Ph.D.)--University of Minnesota, 2006.

This research focuses on the problem of capacity management under uncertainty when a firm needs to satisfy demand with time-differentiated customer classes. The differentiation of customer classes may result from different degrees of urgency, the fact that the firm offers time-based strategies to attract customers, and/or customer choice behavior. We study such problems by exploring three Markov-Decision-Process (MDP) formulations that are inspired by real applications. In the first application, we examine the implication of outsourcing on a firm's capacity decisions. In the second application, we consider contract manufacturers who attempt to maximize profits by selling capacity under different contract terms to different buyers. The third application relates to advanced access in primary care clinics, which is a capacity management approach designed to reduce wait and improve patients' access to their designated physicians. All three problems are formulated in the multi-dimensional MDP framework. We analyze the resulting models and characterize the nature of the optimal capacity management policies. When optimal policies are hard to compute/implement, we propose scalable heuristics and obtain bounds. Numerical examples that confirm theoretical results and provide additional insights are reported.

ISBN: 9780542735493Subjects--Topical Terms:

626639
Engineering, Industrial.

Stochastic models for capacity management in presence of time-differentiated customer classes.
LDR:02194nam 2200265 a 45 001 974310
005 20110929
008 110929s2006 eng d
020 $a 9780542735493
035 $a (UMI)AAI3220046
035 $a AAI3220046
040 $a UMI $c UMI
100 1 $a Wang, Lei. $3 1256221
245 1 0 $a Stochastic models for capacity management in presence of time-differentiated customer classes.
300 $a 168 p.
500 $a Adviser: Diwakar Gupta.
500 $a Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3378.
502 $a Thesis (Ph.D.)--University of Minnesota, 2006.
520 $a This research focuses on the problem of capacity management under uncertainty when a firm needs to satisfy demand with time-differentiated customer classes. The differentiation of customer classes may result from different degrees of urgency, the fact that the firm offers time-based strategies to attract customers, and/or customer choice behavior. We study such problems by exploring three Markov-Decision-Process (MDP) formulations that are inspired by real applications. In the first application, we examine the implication of outsourcing on a firm's capacity decisions. In the second application, we consider contract manufacturers who attempt to maximize profits by selling capacity under different contract terms to different buyers. The third application relates to advanced access in primary care clinics, which is a capacity management approach designed to reduce wait and improve patients' access to their designated physicians. All three problems are formulated in the multi-dimensional MDP framework. We analyze the resulting models and characterize the nature of the optimal capacity management policies. When optimal policies are hard to compute/implement, we propose scalable heuristics and obtain bounds. Numerical examples that confirm theoretical results and provide additional insights are reported.
590 $a School code: 0130.
650 4 $a Engineering, Industrial. $3 626639
690 $a 0546
710 2 0 $a University of Minnesota. $3 676231
773 0 $t Dissertation Abstracts International $g 67-06B.
790 $a 0130
790 1 0 $a Gupta, Diwakar, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3220046