Advanced Sales Route Optimization Through Enhanced Genetic Algorithms and Real-Time Navigation Systems
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| Publicado en: | Algorithms vol. 18, no. 5 (2025), p. 260 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/a18050260 |2 doi | |
| 035 | |a 3211847024 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Cunuhay Cuchipe Wilmer Clemente |u Faculty of Engineering and Applied Sciences, Technical University of Cotopaxi, La Maná Extension, La Maná 050201, Ecuador; wilmer.cunuhay@utc.edu.ec (W.C.C.C.); johnny.bajana@utc.edu.ec (J.B.Z.) | |
| 245 | 1 | |a Advanced Sales Route Optimization Through Enhanced Genetic Algorithms and Real-Time Navigation Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) that integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model to enable real-time, intelligent route planning. The approach addresses the limitations of traditional genetic algorithms by enhancing solution quality, maintaining population diversity, and incorporating data-driven traffic estimations via deep learning. Experimental results on real-world data from the NYC Taxi dataset show that GAAM-TS significantly outperforms both Standard GA and GA-AM variants, achieving up to 20% improvement in travel efficiency while maintaining robustness across problem sizes. Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. The proposed methodology is applicable to last-mile delivery, fleet routing, and sales territory management, offering a scalable and adaptive solution. Future work will explore parallelization strategies and multi-objective extensions for sustainability-aware routing. | |
| 653 | |a Navigation systems | ||
| 653 | |a Deep learning | ||
| 653 | |a Adaptability | ||
| 653 | |a Mathematical models | ||
| 653 | |a Traffic | ||
| 653 | |a Mutation | ||
| 653 | |a Travel time | ||
| 653 | |a Environmental impact | ||
| 653 | |a Energy consumption | ||
| 653 | |a Sales | ||
| 653 | |a Machine learning | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Route optimization | ||
| 653 | |a Prediction models | ||
| 653 | |a Route planning | ||
| 653 | |a Tabu search | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Travel | ||
| 653 | |a Linear programming | ||
| 653 | |a Customers | ||
| 653 | |a Real time | ||
| 653 | |a Logistics | ||
| 653 | |a Traveling salesman problem | ||
| 700 | 1 | |a Zajia, Johnny Bajaña |u Faculty of Engineering and Applied Sciences, Technical University of Cotopaxi, La Maná Extension, La Maná 050201, Ecuador; wilmer.cunuhay@utc.edu.ec (W.C.C.C.); johnny.bajana@utc.edu.ec (J.B.Z.) | |
| 700 | 1 | |a Oviedo Byron |u Faculty of Graduate Programs, State Technical University of Quevedo, Quevedo 120503, Ecuador; boviedo@uteq.edu.ec | |
| 700 | 1 | |a Zambrano-Vega, Cristian |u Faculty of Engineering Sciences, State Technical University of Quevedo, Quevedo 120503, Ecuador | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 5 (2025), p. 260 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211847024/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211847024/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211847024/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |