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Due to the difficulty of solving to optimality large-scale instances of vehicle routing problems, a significant research effort has been dedicated to [[metaheuristic]]s such as [[Genetic algorithms]], [[Tabu search]], [[Simulated annealing]] and Adaptive Large Neighborhood Search (ALNS). Some of the most recent and efficient metaheuristics for vehicle routing problems reach solutions within 0.5% or 1% of the optimum for problem instances counting hundreds or thousands of delivery points.<ref>{{cite journal|vauthors=Vidal T, Crainic TG, Gendreau M, Prins C|year=2014|title=A unified solution framework for multi-attribute vehicle routing problems
|journal=European Journal of Operational Research|volume=234|issue=3|pages=658–673|doi=10.1016/j.ejor.2013.09.045|s2cid=21037953 |url=https://www.cirrelt.ca/documentstravail/cirrelt-2012-23.pdf }}</ref> These methods are also more robust in the sense that they can be more easily adapted to deal with a variety of side constraints. As such, the application of metaheuristic techniques is often preferred for large-scale applications with complicating constraints and decision sets.
==See also==
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