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

Integration of the production planni...

Kaskavelis, Christos A.

FindBook

Google Book

Amazon

博客來

Integration of the production planning and control decision process in manufacturing systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Integration of the production planning and control decision process in manufacturing systems./
作者:	Kaskavelis, Christos A.
面頁冊數:	198 p.
附註:	Source: Dissertation Abstracts International, Volume: 59-02, Section: B, page: 0813.
Contained By:	Dissertation Abstracts International59-02B.
標題:	Engineering, Industrial. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9824565
ISBN:	059176685X

Integration of the production planning and control decision process in manufacturing systems.
Kaskavelis, Christos A.

Integration of the production planning and control decision process in manufacturing systems. - 198 p.

Source: Dissertation Abstracts International, Volume: 59-02, Section: B, page: 0813.

Thesis (Ph.D.)--Boston University, 1998.

Recent efforts in research and software development in the Manufacturing Production Planning field, have focused on enterprise-wide Supply Chain Management solutions (SCM). SCM aims at eliminating the most important shortfall of Manufacturing Resource Planning Systems (MRP). MRP relies on a sequential planning algorithm which fails to address simultaneously resource and flow constraints of end products and components. The MRP's backwards explosion of production requirements results in sub-optimal and inflexible solutions. In contrast, SCM Systems rely on concurrent optimization to create a globally superior schedule. As a result, the Enterprise problem size increases significantly. The common shortcut is the arbitrary and unstructured elimination or aggregation of important production system dynamics.

ISBN: 059176685XSubjects--Topical Terms:

626639
Engineering, Industrial.

Integration of the production planning and control decision process in manufacturing systems.
LDR:03522nmm 2200313 4500 001 1854483
005 20040528102334.5
008 130614s1998 eng d
020 $a 059176685X
035 $a (UnM)AAI9824565
035 $a AAI9824565
040 $a UnM $c UnM
100 1 $a Kaskavelis, Christos A. $3 1942321
245 1 0 $a Integration of the production planning and control decision process in manufacturing systems.
300 $a 198 p.
500 $a Source: Dissertation Abstracts International, Volume: 59-02, Section: B, page: 0813.
500 $a Major Professor: M. C. Caramanis.
502 $a Thesis (Ph.D.)--Boston University, 1998.
520 $a Recent efforts in research and software development in the Manufacturing Production Planning field, have focused on enterprise-wide Supply Chain Management solutions (SCM). SCM aims at eliminating the most important shortfall of Manufacturing Resource Planning Systems (MRP). MRP relies on a sequential planning algorithm which fails to address simultaneously resource and flow constraints of end products and components. The MRP's backwards explosion of production requirements results in sub-optimal and inflexible solutions. In contrast, SCM Systems rely on concurrent optimization to create a globally superior schedule. As a result, the Enterprise problem size increases significantly. The common shortcut is the arbitrary and unstructured elimination or aggregation of important production system dynamics.
520 $a We propose a "bottom-up" approach that introduces Cellular Manufacturing as a means of natural decomposition of the system. Production Cells are defined as subsystems with common responsibilities (types of parts produced) or functionalities (types of resources owned). Decision support tools at each Production Cells use standard methods to define very precisely the best a-priori allocation of labor to resources and resources to part operations. This is based on novel Standard Work Design algorithms we propose and develop. After Production Cell Standard Work Design is performed, an enterprise-wide Production Planning algorithm is employed to synchronize cell-specific production requirements. Unlike most SCM approaches, which aggregate arbitrarily and ignore important constraints, the important characteristics of each Cell and its production and process idiosyncrasies are modeled explicitly at the Production Cell level and passed on to the Enterprise-wide algorithm. Thus, the resulting Cell specific production requirements are feasible and can be trusted by the Cell personnel. The Enterprise Level problem is solved using Linear Programming (LP) and provides sensitivity information that can be used to reallocate capacity across Cells. Alternative LP formulations and information exchange mechanisms are described and compared. Finally, each Cell tracks its production targets by employing detailed Scheduling algorithms.
520 $a We also compare against current industry practices (MRP) on industry size problems. Significant benefits in cycle time reduction and delivery performance are reported. In addition, a flexible enterprise data model is proposed for storing and exchanging information and software design considerations are addressed.
590 $a School code: 0017.
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Operations Research. $3 626629
650 4 $a Engineering, System Science. $3 1018128
690 $a 0546
690 $a 0796
690 $a 0790
710 2 0 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 59-02B.
790 1 0 $a Caramanis, M. C., $e advisor
790 $a 0017
791 $a Ph.D.
792 $a 1998
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9824565