Optimization-Oriented Global Constraints
Constraints
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A Hybrid Exact Algorithm for the TSPTW
INFORMS Journal on Computing
Depth-bounded discrepancy search
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Improved limited discrepancy search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Improving CP-based local branching via sliced neighborhood search
Proceedings of the 2011 ACM Symposium on Applied Computing
Expert Systems with Applications: An International Journal
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Limited Discrepancy Search (LDS) is one of the most widely used search strategies in Constraint Programming; given a solution suggested by a search heuristics, LDS explores the search space at increasing discrepancy with respect to such solution. In optimization problems, LDS is used as a way to explore the k-distance neighborhood of an incumbent solution using constraint propagation and tree search. However, for large problems, the size of the resulting neighborhoods limits the k-distance (i.e. the number of discrepancies) that can be efficiently explored. If the first solution is far from the optimal one, exploring limited neighborhood leads to small improvements. Therefore, we propose a variant of LDS that samples the LDS space by exploring slices of (possibly very large) discrepancy based neighborhoods. Instead of deciding only the number of variables that can change (the k- distance) we decide which n-k variables should be fixed to the value they have in the incumbent solution. We present results on hard Asymmetric Traveling Salesman Problem with Time Windows (ATSPTW) instances to show the effectiveness of the approach.