Abstract
In this paper we introduce a new method based on Russian Doll Search (RDS) for solving optimization problems expressed as Valued Constraint Satisfaction Problems (VCSPs). The RDS method solves problems of size n (where n is the number of variables) by replacing one search by n successive searches on nested subproblems using the results of each search to produce a better lower bound. The main idea of our method is to introduce the variables through the successive searches not one by one but by sets of k variables. We present two variants of our method: the first one where the number k is fixed, noted k sub(f ) RDS; the second one, k sub(v)RDS, where k can be variable. Finally, we show that our method improves RDS on daily management of an earth observation satellite.