A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours

Caio César Freitas (), Dario José Aloise (), Fábio Francisco Costa Fontes (), Andréa Cynthia Santos () and Matheus Silva Menezes ()
Additional contact information
Caio César Freitas: Universidade do Estado do Rio Grande do Norte (UERN), Universidade Federal Rural do Semi-Árido (UFERSA)
Dario José Aloise: Universidade do Estado do Rio Grande do Norte (UERN), Universidade Federal Rural do Semi-Árido (UFERSA)
Fábio Francisco Costa Fontes: Universidade do Estado do Rio Grande do Norte (UERN), Universidade Federal Rural do Semi-Árido (UFERSA)
Andréa Cynthia Santos: Université Le Havre Normandie
Matheus Silva Menezes: Universidade do Estado do Rio Grande do Norte (UERN), Universidade Federal Rural do Semi-Árido (UFERSA)

OR Spectrum: Quantitative Approaches in Management, 2023, vol. 45, issue 3, No 5, 903-924

Abstract: Abstract In this article, a solution is proposed through a population-based metaheuristic for the Two-level Hub Location Routing Problem with Directed Tours (THLRP-DT). Hubs are facilities used to handle and dispatch resources on a given network. The goal of the THLRP-DT is to locate a set of hubs on a network and to route resources from sources to destinations, where the hubs are connected by means of an oriented cycle, and the spokes form clusters. Each cluster is composed of a unique hub, including none or some spoke nodes, connected in an oriented cycle structure. This problem appears in transportation logistics, where the flow of demands can be aggregated, resulting in economies of scale, and the orientations of arcs model a one way flow direction, which speeds up the distribution. We propose a Biased Random-Key Genetic Algorithm (BRKGA) metaheuristic, where the parameters have been calibrated using a machine learning package, which makes use of a machine learning mechanism. The results obtained using the BRKGA metaheuristic are of high quality compared to the ones found in the literature, improving solutions for instances with unknown optimal values.

Keywords: Hub location routing problem; Combinatorial optimization; BRKGA metaheuristic; Logistics (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00291-023-00718-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:orspec:v:45:y:2023:i:3:d:10.1007_s00291-023-00718-y

Ordering information: This journal article can be ordered from
http://www.springer. ... research/journal/291

DOI: 10.1007/s00291-023-00718-y

Access Statistics for this article

OR Spectrum: Quantitative Approaches in Management is currently edited by Rainer Kolisch

More articles in OR Spectrum: Quantitative Approaches in Management from Springer, Gesellschaft für Operations Research e.V.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().