Vehicle routing: less 'artificial', more 'intelligence'

摘要

The integration of multiple constraints of the Vehicle Routing Problem (VRP) variants is computationally expensive. Although vehicle routing problems have been well researched, variants are typically treated in isolation, whereas industry requires integrated solutions. Solution algorithms are also tested using benchmark data that are questionable, and that do not represent typical applications. The paper proposes an approach that solves a problem by analyzing its environment through cluster analysis, chooses an appropriate solution strategy, and tests the results in airl attempt to learn for the purposes of improved future decisions.