MCS

MCS---a new algorithm for multicriteria optimisation in constraint programming We propose a new algorithm called MCS for the search for solutions to multicriteria combinatorial optimisation problems. To quickly produce a solution that offers a good trade-off between criteria, the MCS algorithm alternates several Branch & Bound searches following diversified search strategies. It is implemented in CP in a dedicated framework and can be specialised for either complete or partial search.

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References in zbMATH (referenced in 8 articles )

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  1. Bernardi, Mauro; Catania, Leopoldo: Comparison of value-at-risk models using the MCS approach (2016)
  2. Johnes, Jill: Operational research in education (2015)
  3. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  4. Timonin, Mikhail: Maximization of the Choquet integral over a convex set and its application to resource allocation problems (2012)
  5. Qu, R.; Burke, E.K.; McCollum, B.; Merlot, L.T.G.; Lee, S.Y.: A survey of search methodologies and automated system development for examination timetabling (2009)
  6. Qu, Rong; Burke, Edmund K.; McCollum, Barry: Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems (2009)
  7. Le Huédé, F.; Grabisch, M.; Labreuche, C.; Savéant, P.: MCS---a new algorithm for multicriteria optimisation in constraint programming (2006)
  8. Grabisch, Michel: Alternative representations of discrete fuzzy measures for decision making (1997)