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Dynamic Vehicle Routing Problem: A Monte Carlo approach
First, that we know a bounding rectangle of the area in which the requests might appear.
A Particle Swarm Optimization hyper-heuristic for the Dynamic Vehicle Routing Problem
The optimization algorithm is chosen on the basis of a prediction made by a linear model trained on that data and the relative results obtained by the optimization algorithms.
Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples
In comparison with the original GAPSO formulation it includes the following four features: a global restart management scheme, samples gathering within an R-Tree based index (archive/memory of samples), adaptation of a sampling behavior based on a global particle performance, and a specific approach to local search.
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