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New filtering for AtMostNValue and its weighted variant: A Lagrangian approach

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Abstract

The AtMostNValue global constraint, which restricts the maximum number of distinct values taken by a set of variables, is a well known NP-Hard global constraint. The weighted version of the constraint, AtMostWValue, where each value is associated with a weight or cost, is a useful and natural extension. Both constraints occur in many industrial applications where the number and the cost of some resources have to be minimized. This paper introduces a new filtering algorithm based on a Lagrangian relaxation for both constraints. This contribution is illustrated on problems related to facility location, which is a fundamental class of problems in operations research and management sciences. Preliminary evaluations show that the filtering power of the Lagrangian relaxation can provide significant improvements over the state-of-the-art algorithm for these constraints. We believe it can help to bridge the gap between constraint programming and linear programming approaches for a large class of problems related to facility location.

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Correspondence to Hadrien Cambazard.

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Cambazard, H., Fages, JG. New filtering for AtMostNValue and its weighted variant: A Lagrangian approach. Constraints 20, 362–380 (2015). https://doi.org/10.1007/s10601-015-9191-0

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