# A survey on combinatorial optimization in dynamic environments∗

Nicolas Boria; Vangelis T. Paschos

RAIRO - Operations Research (2011)

- Volume: 45, Issue: 3, page 241-294
- ISSN: 0399-0559

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topBoria, Nicolas, and Paschos, Vangelis T.. "A survey on combinatorial optimization in dynamic environments∗." RAIRO - Operations Research 45.3 (2011): 241-294. <http://eudml.org/doc/276362>.

@article{Boria2011,

abstract = {This survey presents major results and issues related to the study of NPO problems in dynamic environments, that is, in settings where instances are allowed to undergo some modifications over time. In particular, the survey focuses on two complementary frameworks. The first one is the reoptimization framework, where an instance I that is already solved undergoes some local perturbation. The goal is then to make use of the information provided by the initial solution to compute a new solution. The second framework is probabilistic optimization, where the instance to optimize is not fully known at the time when a solution is to be proposed, but results from a determined Bernoulli process. Then, the goal is to compute a solution with optimal expected value.},

author = {Boria, Nicolas, Paschos, Vangelis T.},

journal = {RAIRO - Operations Research},

keywords = {Approximation; reoptimization; hereditary problem; complexity; graph; on-line algorithms; probabilistic optimization; approximation},

language = {eng},

month = {12},

number = {3},

pages = {241-294},

publisher = {EDP Sciences},

title = {A survey on combinatorial optimization in dynamic environments∗},

url = {http://eudml.org/doc/276362},

volume = {45},

year = {2011},

}

TY - JOUR

AU - Boria, Nicolas

AU - Paschos, Vangelis T.

TI - A survey on combinatorial optimization in dynamic environments∗

JO - RAIRO - Operations Research

DA - 2011/12//

PB - EDP Sciences

VL - 45

IS - 3

SP - 241

EP - 294

AB - This survey presents major results and issues related to the study of NPO problems in dynamic environments, that is, in settings where instances are allowed to undergo some modifications over time. In particular, the survey focuses on two complementary frameworks. The first one is the reoptimization framework, where an instance I that is already solved undergoes some local perturbation. The goal is then to make use of the information provided by the initial solution to compute a new solution. The second framework is probabilistic optimization, where the instance to optimize is not fully known at the time when a solution is to be proposed, but results from a determined Bernoulli process. Then, the goal is to compute a solution with optimal expected value.

LA - eng

KW - Approximation; reoptimization; hereditary problem; complexity; graph; on-line algorithms; probabilistic optimization; approximation

UR - http://eudml.org/doc/276362

ER -

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