Tackling the rich vehicle routing problem with nature-inspired algorithms

Please always quote using this URN: urn:nbn:de:bvb:20-opus-268942
  • 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-worldIn 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.show moreshow less

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Metadaten
Author: Veronika Lesch, Maximilian König, Samuel Kounev, Anthony Stein, Christian Krupitzer
URN:urn:nbn:de:bvb:20-opus-268942
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Language:English
Parent Title (English):Applied Intelligence
ISSN:1573-7497
Year of Completion:2022
Volume:52
Pagenumber:9476–9500
Source:Applied Intelligence 2022, 52:9476–9500. DOI: 10.1007/s10489-021-03035-5
DOI:https://doi.org/10.1007/s10489-021-03035-5
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Tag:ant-colony optimization; genetic algorithm; logistics; real-world application; rich vehicle routing problem
Release Date:2022/06/10
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International