Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Computers and Operations Research
Constraint-Based Scheduling
Optimization-Oriented Global Constraints
Constraints
A Constraint Programming Framework for Local Search Methods
Journal of Heuristics
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
Constraint and Integer Programming: Toward a Unified Methodology (Operations Research/Computer Science Interfaces", 27)
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
Constraint-Based Local Search
An Evolutionary Algorithm for Polishing Mixed Integer Programming Solutions
INFORMS Journal on Computing
Finding diverse and similar solutions in constraint programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
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
Automated configuration of mixed integer programming solvers
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
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Local branching is a general purpose heuristic method which searches locally around the best known solution by employing tree search. It has been successfully used in Mixed-Integer Programming where local branching constraints are used to model the neighborhood of an incumbent solution and improve the bound. We propose the integration of local branching in Constraint Programming (CP). This integration is not simply a matter of implementation, but requires a number of significant extensions. The original contributions of this paper are: the definition of an efficient and incremental bound computation for the neighborhood, a cost-based filtering algorithm for the local branching constraint and a novel diversification strategy that can explore arbitrarily far regions of the search tree w.r.t. the already found solutions. We demonstrate the practical value of local branching in CP by providing an extensive experimental evaluation on the hard instances of the Asymmetric Traveling Salesman Problem with Time Windows.