Centralized supply chain network optimization with UAV-based last mile deliveries

This paper proposes a centralized supply chain network optimization model that maximizes the total profit obtained by a company that produces and/or outsources production, stores, ships and sells products to customers using a fleet made up of trucks and, in the last mile, also of drones. The model i...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Transportation research. Part C, Emerging technologies Emerging technologies, 2023-10, Vol.155, p.104316, Article 104316
Hauptverfasser: Colajanni, Gabriella, Daniele, Patrizia, Nagurney, Anna
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper proposes a centralized supply chain network optimization model that maximizes the total profit obtained by a company that produces and/or outsources production, stores, ships and sells products to customers using a fleet made up of trucks and, in the last mile, also of drones. The model includes realistic features of unmanned aerial vehicles (UAVs) in the form of drones with fundamental limitations such as low battery capacities and short delivery ranges. The constrained nonlinear optimization problem is formulated as a variational inequality. Existence and uniqueness results for the solution of the variational inequality are provided along with the results of detailed numerical simulations that emphasize the advantages of the use of a hybrid fleet from enhanced profits to reduction in air pollution. Our quantitative results reveal great promise and insights for the logistics industry in the use of emerging UAV technologies for last mile parcel deliveries as a practical solution within a holistic supply chain network context. •Supply chain network for last mile delivery with trucks and/or drones.•System-Optimization.•Nonlinear optimization.•Variational inequalities.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2023.104316