Ant colony optimization to solve the rescue problem as a vehicle routing problem with hard time windows
Kein Vorschaubild vorhanden
Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2022
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings of international joint conference on advances in computational intelligence. Algorithms for intelligent systems
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
53-65
Patentnummer
Verlag / Herausgebende Institution
Springer
Verlagsort / Veranstaltungsort
Singapore
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
The rescue problem is an adaptation of a standard Vehicle Routing Problem where a set of patients suffering from various medical conditions has to be picked up by a set of ambulances and brought back to the hospital. Optimizing this problem is important to improve the use of life emergency vehicles in daily or disaster situations. Although this problem is usually modeled as a Capacitated Vehicle Routing Problem, different formulations are proposed in the literature including multi-objective optimization with shortest route and maximization of the number of patients that will survive or remain stable. Ant Colony Optimization (ACO) and Genetic Algorithms (GA) are frequently used, where ACO performs better on objectives specific to the rescue problem. We model the problem as a single-objective Vehicle Routing Problem with Time Windows (VRPTW) using hard time windows. Each patient is assigned a degree of injury and a corresponding maximum time window. An immediate return to the hospital for critically injured patients is also introduced. The rescue problem turns to a VRPTW with hard time windows for different problem sizes and is solved with ACO. The results suggest that with a sufficiently large fleet, it can be ensured that critically injured patients are reached in good time.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
5th International Joint Conference on Advances in Computational Intelligence (IJCACI 2021)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
23.10.2021
Enddatum der Konferenz
24.10.2021
Datum der letzten Prüfung
ISBN
978-981-19-0331-1
978-981-19-0332-8
978-981-19-0332-8
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
SUPPAN, Mélanie, Thomas HANNE und Rolf DORNBERGER, 2022. Ant colony optimization to solve the rescue problem as a vehicle routing problem with hard time windows. In: Mohammad Shorif UDDIN, Prashant Kumar JAMWAL und Jagdish Chand BANSAL (Hrsg.), Proceedings of international joint conference on advances in computational intelligence. Algorithms for intelligent systems. Singapore: Springer. 2022. S. 53–65. ISBN 978-981-19-0331-1. Verfügbar unter: https://irf.fhnw.ch/handle/11654/48200