Hybrid metaheuristics for stochastic constraint programming

Creative Commons License

Prestwich S. D., Tarim Ş. A., Rossi R., Hnich B.

CONSTRAINTS, cilt.20, sa.1, ss.57-76, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 1
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1007/s10601-014-9170-x
  • Dergi Adı: CONSTRAINTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.57-76
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods, and exploits standard filtering algorithms to handle hard constraints more efficiently. For problems with many scenarios it can be combined with scenario reduction and sampling methods.