Tractable algorithms for chance-constrained combinatorial problems

Olivier Klopfenstein (1, 2)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Orange Labs R&D [Rennes]

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.

Domaines: Recherche opérationnelle [math.OC]

Olivier Klopfenstein. Tractable algorithms for chance-constrained combinatorial problems. RAIRO - Operations Research, 2009, 43 (2), pp.157-187. ⟨10.1051/ro/2009010⟩. ⟨hal-00452599⟩