AN ANALYSIS OF CAPICITY EXPANSION PROBLEMS WITH BACKORDERS AND STOCHASTIC DEMAND

Persistent Link:
http://hdl.handle.net/10150/292045
Title:
AN ANALYSIS OF CAPICITY EXPANSION PROBLEMS WITH BACKORDERS AND STOCHASTIC DEMAND
Author:
Huang, Chih-yüan
Issue Date:
1987
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
We show that, under certain conditions, instead of solving stochastic capacity expansion problems, we will obtain the same optimal solution by solving deterministic equivalent problems. Since only the first decision must be implemented immediately, knowing the optimal first decision is nearly as good as knowing the entire optimal sequences. Hence if we can solve the problem with 'big enough' finite horizon such that the first decision remains optimal for longer than this finite horizon, then we identify the 'big enough' finite horizon as forecast horizon. The forward dynamic programming recursion can be used to solve a finite horizon problem. An efficient forward algorithm has been developed to obtain the first optimal decision and forecast horizon. A heuristic algorithm also has been derived to prove an initial decision is within known error bound of the optimal first decision. Several examples are examined to investigate how a decision will be affected by randomness. (Abstract shortened with permission of author.)
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Industrial capacity -- Mathematical models.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Systems and Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Higle, Julia L.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAN ANALYSIS OF CAPICITY EXPANSION PROBLEMS WITH BACKORDERS AND STOCHASTIC DEMANDen_US
dc.creatorHuang, Chih-yüanen_US
dc.contributor.authorHuang, Chih-yüanen_US
dc.date.issued1987en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractWe show that, under certain conditions, instead of solving stochastic capacity expansion problems, we will obtain the same optimal solution by solving deterministic equivalent problems. Since only the first decision must be implemented immediately, knowing the optimal first decision is nearly as good as knowing the entire optimal sequences. Hence if we can solve the problem with 'big enough' finite horizon such that the first decision remains optimal for longer than this finite horizon, then we identify the 'big enough' finite horizon as forecast horizon. The forward dynamic programming recursion can be used to solve a finite horizon problem. An efficient forward algorithm has been developed to obtain the first optimal decision and forecast horizon. A heuristic algorithm also has been derived to prove an initial decision is within known error bound of the optimal first decision. Several examples are examined to investigate how a decision will be affected by randomness. (Abstract shortened with permission of author.)en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectIndustrial capacity -- Mathematical models.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHigle, Julia L.en_US
dc.identifier.proquest1332234en_US
dc.identifier.oclc18899660en_US
dc.identifier.bibrecord.b16665442en_US
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