Tackling the rich vehicle routing problem with nature-inspired algorithms
Zitieren Sie bitte immer diese 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.…
Autor(en): | Veronika Lesch, Maximilian König, Samuel Kounev, Anthony Stein, Christian Krupitzer |
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URN: | urn:nbn:de:bvb:20-opus-268942 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Applied Intelligence |
ISSN: | 1573-7497 |
Erscheinungsjahr: | 2022 |
Band / Jahrgang: | 52 |
Seitenangabe: | 9476–9500 |
Originalveröffentlichung / Quelle: | Applied Intelligence 2022, 52:9476–9500. DOI: 10.1007/s10489-021-03035-5 |
DOI: | https://doi.org/10.1007/s10489-021-03035-5 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Freie Schlagwort(e): | ant-colony optimization; genetic algorithm; logistics; real-world application; rich vehicle routing problem |
Datum der Freischaltung: | 10.06.2022 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |