Algorithms for a temporal decoupling problem in multi-agent planning
Eighteenth national conference on Artificial intelligence
Constraint Processing
Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows
Journal of Heuristics
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
A Guided Cooperative Search for the Vehicle Routing Problem with Time Windows
IEEE Intelligent Systems
Active guided evolution strategies for large-scale vehicle routing problems with time windows
Computers and Operations Research
A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows
Transportation Science
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Spatial, temporal, and hybrid decompositions for large-scale vehicle routing with time windows
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Randomized adaptive vehicle decomposition for large-scale power restoration
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Maximum-throughput mapping of SDFGs on multi-core SoC platforms
Journal of Parallel and Distributed Computing
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In recent years, the size of combinatorial applications and the need to produce high-quality solutions quickly have increased steadily, providing significant challenges for optimization algorithms. This paper addresses this issue for large-scale vehicle routing problems with time windows, a class of very difficult optimization problems involving complex spatial and temporal dependencies. It proposes a randomized adaptive spatial decoupling (RASD) scheme for vehicle routing with time windows in order to produce high-quality solutions quickly. Experimental results on hard instances with 1,000 customers and 90 vehicles show that the RASD scheme, together with large neighborhood search, significantly improves the quality of the solutions under time constraints. Interestingly, the RASD scheme, when allowed to run longer, also improves the best available solutions in almost all the tested instances.