A dimension-reduction algorithm for multi-stage decision problems with returns in a partially ordered set
Teodros Getachew; Michael M. Kostreva
RAIRO - Operations Research - Recherche Opérationnelle (2002)
- Volume: 36, Issue: 3, page 175-190
- ISSN: 0399-0559
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topGetachew, Teodros, and Kostreva, Michael M.. "A dimension-reduction algorithm for multi-stage decision problems with returns in a partially ordered set." RAIRO - Operations Research - Recherche Opérationnelle 36.3 (2002): 175-190. <http://eudml.org/doc/245158>.
@article{Getachew2002,
abstract = {In this paper a two-stage algorithm for finding non- dominated subsets of partially ordered sets is established. A connection is then made with dimension reduction in time-dependent dynamic programming via the notion of a bounding label, a function that bounds the state-transition cost functions. In this context, the computational burden is partitioned between a time-independent dynamic programming step carried out on the bounding label and a direct evaluation carried out on a subset of “real” valued decisions. A computational application to time-dependent fuzzy dynamic programming is presented.},
author = {Getachew, Teodros, Kostreva, Michael M.},
journal = {RAIRO - Operations Research - Recherche Opérationnelle},
keywords = {multi-criteria optimization; time-variant networks; dimension reduction},
language = {eng},
number = {3},
pages = {175-190},
publisher = {EDP-Sciences},
title = {A dimension-reduction algorithm for multi-stage decision problems with returns in a partially ordered set},
url = {http://eudml.org/doc/245158},
volume = {36},
year = {2002},
}
TY - JOUR
AU - Getachew, Teodros
AU - Kostreva, Michael M.
TI - A dimension-reduction algorithm for multi-stage decision problems with returns in a partially ordered set
JO - RAIRO - Operations Research - Recherche Opérationnelle
PY - 2002
PB - EDP-Sciences
VL - 36
IS - 3
SP - 175
EP - 190
AB - In this paper a two-stage algorithm for finding non- dominated subsets of partially ordered sets is established. A connection is then made with dimension reduction in time-dependent dynamic programming via the notion of a bounding label, a function that bounds the state-transition cost functions. In this context, the computational burden is partitioned between a time-independent dynamic programming step carried out on the bounding label and a direct evaluation carried out on a subset of “real” valued decisions. A computational application to time-dependent fuzzy dynamic programming is presented.
LA - eng
KW - multi-criteria optimization; time-variant networks; dimension reduction
UR - http://eudml.org/doc/245158
ER -
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