TY  - JOUR
A1  - Lesch, Veronika
A1  - König, Maximilian
A1  - Kounev, Samuel
A1  - Stein, Anthony
A1  - Krupitzer, Christian
T1  - Tackling the rich vehicle routing problem with nature-inspired algorithms
JF  - Applied Intelligence
N2  - 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.
KW  - logistics
KW  - rich vehicle routing problem
KW  - ant-colony optimization
KW  - genetic algorithm
KW  - real-world application
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268942
SN  - 1573-7497
VL  - 52
ER  -