# Tractable algorithms for chance-constrained combinatorial problems

RAIRO - Operations Research (2009)

- Volume: 43, Issue: 2, page 157-187
- ISSN: 0399-0559

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topKlopfenstein, Olivier. "Tractable algorithms for chance-constrained combinatorial problems." RAIRO - Operations Research 43.2 (2009): 157-187. <http://eudml.org/doc/250652>.

@article{Klopfenstein2009,

abstract = {
This paper aims at proposing tractable algorithms to find effectively good solutions to large size chance-constrained combinatorial problems. A new robust model is introduced to deal with uncertainty in mixed-integer linear problems. It is shown to be strongly related to chance-constrained programming when considering pure 0–1 problems. Furthermore, its tractability is highlighted. Then, an optimization algorithm is designed to provide possibly good solutions to chance-constrained combinatorial problems. This approach is numerically tested on knapsack and multi-dimensional knapsack problems. The results obtained outperform many methods based on earlier literature.
},

author = {Klopfenstein, Olivier},

journal = {RAIRO - Operations Research},

keywords = {Integer linear programming; chance constraints; robust optimization; heuristic.; integer linear programming; heuristic},

language = {eng},

month = {4},

number = {2},

pages = {157-187},

publisher = {EDP Sciences},

title = {Tractable algorithms for chance-constrained combinatorial problems},

url = {http://eudml.org/doc/250652},

volume = {43},

year = {2009},

}

TY - JOUR

AU - Klopfenstein, Olivier

TI - Tractable algorithms for chance-constrained combinatorial problems

JO - RAIRO - Operations Research

DA - 2009/4//

PB - EDP Sciences

VL - 43

IS - 2

SP - 157

EP - 187

AB -
This paper aims at proposing tractable algorithms to find effectively good solutions to large size chance-constrained combinatorial problems. A new robust model is introduced to deal with uncertainty in mixed-integer linear problems. It is shown to be strongly related to chance-constrained programming when considering pure 0–1 problems. Furthermore, its tractability is highlighted. Then, an optimization algorithm is designed to provide possibly good solutions to chance-constrained combinatorial problems. This approach is numerically tested on knapsack and multi-dimensional knapsack problems. The results obtained outperform many methods based on earlier literature.

LA - eng

KW - Integer linear programming; chance constraints; robust optimization; heuristic.; integer linear programming; heuristic

UR - http://eudml.org/doc/250652

ER -

## References

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