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Naslov:Two-echelon drone–truck collaborative TSP-based routing for humanitarian logistics with time windows and stochastic demand
Avtorji:ID Xiao, N. (Avtor)
ID Lan, H. (Avtor)
Datoteke:.pdf APEM20-3_351-368.pdf (975,35 KB)
MD5: 02C1340B0B25CDBFD9C604C1805B9C17
 
URL https://apem-journal.org/Archives/2025/Abstract-APEM20-3_351-368.html
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:In humanitarian logistics emergency material transportation and distribution, trucks offer large load capacity and long driving range, whereas drone transportation is independent of ground road conditions but constrained by battery life and payload capacity. The coordination of the two can therefore provide complementary advantages. In this paper, the traveling salesman problem is formulated for a two-echelon emergency material distribution process, spanning transportation from the central warehouse to the distribution center and then to the demand points. In the first stage, transportation from the central warehouse to the distribution center is performed by trucks. In the second stage, trucks and drones collaboratively carry out material distribution from the distribution center to the demand points. Based on the above scenario, this paper aims to minimize the total cost of completing all distribution tasks. The model considers capacity constraints at distribution centers, time window constraints at demand points, and stochastic demand, and establishes a two-echelon traveling salesman problem for humanitarian logistics with truck–drone collaboration. Based on the particle swarm optimization (PSO) framework, a heuristic algorithm named PSO-VD is proposed, which transforms the discrete traveling salesman problem into a continuous encoding and integrates drone routes into truck routes using the 2-opt method. In small-scale instances, the solutions obtained by PSO-VD are compared with those of commercial solvers, demonstrating that the proposed algorithm achieves high accuracy with low computational time. For instances with up to 12 demand points, the algorithm obtains solutions within 150 seconds, with an accuracy deviation of less than 10 % compared to exact solution methods. The applicability of the algorithm proposed in this paper has been demonstrated through large-scale numerical examples. Sensitivity analyses are conducted on key parameters, including the time window penalty coefficient, drone speed, and drone battery capacity, yielding practical managerial insights.
Ključne besede:humanitarian logistics, two-echelon routing, drone–vehicle collaboration, stochastic demand, time windows, capacity constraint, vehicle routing problem, VRP, travelling salesman problem, TSP, heuristic algorithm, particle swarm optimization, PSO
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:01.09.2025
Datum sprejetja članka:09.10.2025
Datum objave:31.10.2025
Založnik:Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering
Leto izida:2025
Št. strani:str. 351-368
Številčenje:Vol. 20, no. 3
PID:20.500.12556/DKUM-96682 Novo okno
UDK:658.5
COBISS.SI-ID:265828611 Novo okno
DOI:10.14743/apem2025.3.545 Novo okno
ISSN pri članku:1854-6250
Datum objave v DKUM:23.01.2026
Število ogledov:174
Število prenosov:9
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
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Gradivo je del revije

Naslov:Advances in production engineering & management
Skrajšan naslov:Adv produc engineer manag
Založnik:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:humanitarna logistika, droni - vozila, stohastični ukazi


Zbirka

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  1. Advances in production engineering & management

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