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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Production Planning in Different Stages of a Manufacturing Supply Chain Under Multiple Uncertainties./
Author:	Ramaraj, Goutham.
Description:	1 online resource (93 pages)
Notes:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
Subject:	Operations research. -
Online resource:	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.
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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
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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).
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