國立虎尾科技大學 |

Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties./
作者:	Ramaraj, Goutham.
面頁冊數:	1 online resource (93 pages)
附註:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
標題:	Operations research. -
電子資源:	click for full text (PQDT)
ISBN:	9780355336030

Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties.
Ramaraj, Goutham.

Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties. - 1 online resource (93 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--Iowa State University, 2017.

Includes bibliographical references

This thesis focuses on designing stochastic programming models for production planning at different stages in a manufacturing supply chain under multiple sources of uncertainties. Various decision makers along the manufacturing supply chain often have to make planning decisions with embedded risks and uncertainties. In an effort to reduce risks and to ensure that the customer demand is met in the most efficient and cost effective way, the production plans at each stage need to be strategically planned. To assist production planning decisions, a two-stage stochastic programming model is developed with the objective of minimizing the total cost including production, inventory, and backorder costs. The proposed framework is validated with case studies in an automobile part manufacturer with real data based on literature. The results demonstrate the robustness of the stochastic model compared with various deterministic models. Sensitivity analysis is performed for the production capacity parameter to derive managerial insights regarding lot-sizing and scheduling decisions under different scenarios.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355336030Subjects--Topical Terms:

573517
Operations research.
Index Terms--Genre/Form:

554714
Electronic books.

Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties.
LDR:02333ntm a2200337Ki 4500 001 918936
005 20181106103643.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355336030
035 $a (MiAaPQ)AAI10606896
035 $a (MiAaPQ)iastate:16775
035 $a AAI10606896
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Ramaraj, Goutham. $3 1193392
245 1 0 $a Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties.
264 0 $c 2017
300 $a 1 online resource (93 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Guiping Hu.
502 $a Thesis (M.S.)--Iowa State University, 2017.
504 $a Includes bibliographical references
520 $a This thesis focuses on designing stochastic programming models for production planning at different stages in a manufacturing supply chain under multiple sources of uncertainties. Various decision makers along the manufacturing supply chain often have to make planning decisions with embedded risks and uncertainties. In an effort to reduce risks and to ensure that the customer demand is met in the most efficient and cost effective way, the production plans at each stage need to be strategically planned. To assist production planning decisions, a two-stage stochastic programming model is developed with the objective of minimizing the total cost including production, inventory, and backorder costs. The proposed framework is validated with case studies in an automobile part manufacturer with real data based on literature. The results demonstrate the robustness of the stochastic model compared with various deterministic models. Sensitivity analysis is performed for the production capacity parameter to derive managerial insights regarding lot-sizing and scheduling decisions under different scenarios.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Operations research. $3 573517
650 4 $a Industrial engineering. $3 679492
655 7 $a Electronic books. $2 local $3 554714
690 $a 0796
690 $a 0546
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Iowa State University. $b Industrial and Manufacturing Systems Engineering. $3 1182174
773 0 $t Masters Abstracts International $g 57-02(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10606896 $z click for full text (PQDT)