@article{LeschKoenigKounevetal.2022,
  author    = {Lesch, Veronika and K{\"o}nig, Maximilian and Kounev, Samuel and Stein, Anthony and Krupitzer, Christian},
  title     = {Tackling the rich vehicle routing problem with nature-inspired algorithms},
  series = {Applied Intelligence},
  volume    = {52},
  journal   = {Applied Intelligence},
  issn      = {1573-7497},
  doi       = {10.1007/s10489-021-03035-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-268942},
  pages     = {9476-9500},
  year      = {2022},
  abstract  = {In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.},
  language  = {en}
}