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Article
2022
Tackling the rich vehicle routing problem with nature-inspired algorithms
Tackling the rich vehicle routing problem with nature-inspired algorithms
Abstract (English)
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.
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Applied intelligence, 52 (2022), 9476-9500.
https://doi.org/10.1007/s10489-021-03035-5.
ISSN: 1573-7497
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Lesch, V., König, M., Kounev, S., Stein, A., & Krupitzer, C. (2022). Tackling the rich vehicle routing problem with nature-inspired algorithms. Applied intelligence, 52. https://doi.org/10.1007/s10489-021-03035-5
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English
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000 Computer science, information and general works
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University bibliography
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Sustainable Development Goals
BibTeX
@article{Lesch2022,
doi = {10.1007/s10489-021-03035-5},
author = {Lesch, Veronika and König, Maximilian and Kounev, Samuel et al.},
title = {Tackling the rich vehicle routing problem with nature-inspired algorithms},
journal = {Applied Intelligence},
year = {2022},
volume = {52},
pages = {9476--9500},
}