國立虎尾科技大學 |

Robust Solutions for Geographic Resource Allocation Problems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Robust Solutions for Geographic Resource Allocation Problems./
Author:	Behroozi, Mehdi.
Description:	1 online resource (171 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
Contained By:	Dissertation Abstracts International78-04B(E).
Subject:	Industrial engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781369190762

Robust Solutions for Geographic Resource Allocation Problems.
Behroozi, Mehdi.

Robust Solutions for Geographic Resource Allocation Problems. - 1 online resource (171 pages)

Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

This thesis describes different ways to use robust optimization concepts and techniques in problems that arise in geographic resource allocation. Many problems in geographic resource allocation deal with uncertainty just like many other domains. Some of them, like the k-centers problem, are naturally defined in a minimax fashion, and some others can be treated under uncertainty where we seek robust solutions. Most geographic resource allocation problems can be settled in one or more of the categories, such as location problems, segmentation (partitioning) problems, assignment problems, routing problems, and backbone network design problems . In all such problems there are parameters that can be unknown in practice. It is sensible to define an uncertainty set for the unknown parameter based on some crude knowledge about that unknown parameter and then to treat the uncertainty like some deterministic variability of the values of the parameter, followed by ultimately solving the problem as that parameter is another variable in a higher dimension.

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

Mode of access: World Wide Web

ISBN: 9781369190762Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Robust Solutions for Geographic Resource Allocation Problems.
LDR:03548ntm a2200373Ki 4500 001 909643
005 20180426091042.5
006 m o u
007 cr mn||||a|a||
008 190606s2016 xx obm 000 0 eng d
020 $a 9781369190762
035 $a (MiAaPQ)AAI10164584
035 $a (MiAaPQ)umn:17538
035 $a AAI10164584
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Behroozi, Mehdi. $3 1180521
245 1 0 $a Robust Solutions for Geographic Resource Allocation Problems.
264 0 $c 2016
300 $a 1 online resource (171 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: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
500 $a Adviser: John G. Carlsson.
502 $a Thesis (Ph.D.) $c University of Minnesota $d 2016.
504 $a Includes bibliographical references
520 $a This thesis describes different ways to use robust optimization concepts and techniques in problems that arise in geographic resource allocation. Many problems in geographic resource allocation deal with uncertainty just like many other domains. Some of them, like the k-centers problem, are naturally defined in a minimax fashion, and some others can be treated under uncertainty where we seek robust solutions. Most geographic resource allocation problems can be settled in one or more of the categories, such as location problems, segmentation (partitioning) problems, assignment problems, routing problems, and backbone network design problems . In all such problems there are parameters that can be unknown in practice. It is sensible to define an uncertainty set for the unknown parameter based on some crude knowledge about that unknown parameter and then to treat the uncertainty like some deterministic variability of the values of the parameter, followed by ultimately solving the problem as that parameter is another variable in a higher dimension.
520 $a For such problems in geographic resource allocation, we take a robust approach to tackle the uncertainty. Depending on the problem and also geometry of the uncertainty set, the robust optimization model can be tractable or difficult to solve. We deal with both cases in this thesis where we combine elements from computational geometry, geometric probability theory, vector space optimization, and topology, to either solve the problem to optimality or develop fast algorithms to settle with an approximation solution. We also present a divide and conquer type of approach using geometric partitioning to solve robust optimization problems. In a generic robust optimization problem, if the uncertainty set is an infinite set (which is the case in most practical situations), then we will have an infinite or semi-infinite dimensional optimization problem since the model will have infinite number of constraints. We describe the drawbacks of the current approaches to solving such problems and their inability to obtain reasonable solutions for some special but common and practical cases, like clustered data, and then we show that our approach makes these problems easy to solve.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Operations research. $3 573517
650 4 $a Applied mathematics. $3 1069907
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0796
690 $a 0364
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Minnesota. $b Industrial and Systems Engineering. $3 1180522
773 0 $t Dissertation Abstracts International $g 78-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10164584 $z click for full text (PQDT)