Randomized Adaptive Spatial Decoupling for Large-Scale Vehicle Routing with Time Windows

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.