Mahmoodi, Armin, Sajadi, Seyed Mojtaba, Sadeq, Abdellatif M., Narenji, Masoud and Jasemi, Milad (2025). Enhancing unmanned aerial vehicles logistics for dynamic delivery: a hybrid non-dominated sorting genetic algorithm II with Bayesian belief networks. Annals of Operations Research ,
Abstract
To address the complexities of managing networks of unmanned aerial vehicles (UAVs) and Just-in-Time problem solving, this study introduces a cutting-edge multi-objective location routing optimization model. This model integrates time window constraints, concurrent pickup and delivery demands, and rechargeable battery functionality, significantly enhancing the efficiency of UAV operations. It reduces battery consumption and transportation costs while optimizing delivery times and reducing operational risks. The model improves the refinement of delivery schedules by accounting for uncertain traffic scenarios, thereby increasing its accuracy and reliability in dynamic environments. Additionally, a Bayesian belief networks approach for risk assessment introduces a new layer to operational risk management. The model’s performance and its trade-offs are demonstrated through advanced data visualizations such as 3D Pareto fronts, pair plots, and network graphs, with validation via the NSGA-II approach confirming its reliability and practical applicability. This research represents a major leap forward in drone routing strategies, focusing on efficiency, adaptability, and risk management in UAV logistics and provides a comprehensive framework that bridges the gap between theoretical exploration and practical application.
Publication DOI: | https://doi.org/10.1007/s10479-025-06504-z |
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Divisions: | College of Business and Social Sciences > Aston Business School > Operations & Information Management College of Business and Social Sciences College of Business and Social Sciences > Aston Business School Aston University (General) |
Additional Information: | Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. |
Uncontrolled Keywords: | Bayesian belief networks,Drone delivery,Multi-objective optimization,NSGA-II algorithm,Risk assessment,Routing problem,General Decision Sciences,Management Science and Operations Research |
Publication ISSN: | 1572-9338 |
Last Modified: | 31 Mar 2025 17:41 |
Date Deposited: | 20 Feb 2025 16:43 |
Full Text Link: | |
Related URLs: |
https://link.sp ... 479-025-06504-z
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2025-02-17 |
Accepted Date: | 2025-01-21 |
Authors: |
Mahmoodi, Armin
Sajadi, Seyed Mojtaba ( ![]() Sadeq, Abdellatif M. Narenji, Masoud Jasemi, Milad |