.png)
A Hybrid GRG-Neighborhood Search Model for Dynamic Multi-Depot Vehicle Routing in Disaster Logistics
Abstract
In disaster relief logistics, timely and adaptive routing is critical to meet fluctuating demands and disrupted infrastructure. This paper proposes a Hybrid GRG–Neighbourhood Search (NS) model for solving the Multi-Depot Vehicle Routing Problem with Capacity and Time Dependency (MDVRP-CTD). The model integrates the Generalized Reduced Gradient (GRG) method for handling nonlinear capacity constraints and NS for local route refinement. The objective is to minimize total travel distance, delay penalties, and maximize vehicle utilization under dynamic disaster scenarios. Tested using the SVRPBench dataset, the hybrid model achieved up to 96.5% demand fulfillment, an 11% improvement in vehicle utilization, and a reduction in total distance by 7%, outperforming Tabu Search and ALNS in three simulation scenarios. The model demonstrates enhanced adaptability and responsiveness to time-sensitive, capacity-constrained environments. Its novelty lies in the integration of nonlinear optimization with adaptive local improvement tailored for disaster contexts, providing a robust decision-support tool for real-time humanitarian logistics.
Keywords
Full Text:
PDFReferences
Y. Guo, F. Liu, J.-S. Song, and S. Wang, “Supply chain resilience: A review from the inventory management perspective,” Fundamental Research, vol. 5, no. 2, pp. 450–463, 2025, doi: https://doi.org/10.1016/j.fmre.2024.08.002.
S. Ghaffarian, F. R. Taghikhah, and H. R. Maier, “Explainable artificial intelligence in disaster risk management: Achievements and prospective futures,” International Journal of Disaster Risk Reduction, vol. 98, p. 104123, 2023, doi: https://doi.org/10.1016/j.ijdrr.2023.104123.
S. Pratap and H. M. A. Aziz, “Uncertainty-cognizant post-disaster routing with progressive hedging centered multi meta-heuristic approach,” Transportation Research Part E: Logistics and Transportation Review, vol. 201, p. 104215, 2025, doi: https://doi.org/10.1016/j.tre.2025.104215.
S. Chamurally and J. Rieck, “A practical and robust approach for solving the multi-compartment vehicle routing problem under demand uncertainty using machine learning,” Networks, vol. 84, no. 3, pp. 300–325, 2024, doi: https://doi.org/10.1002/net.22231.
S. Rezvanian, A. Husseinzadeh Kashan, A. Rezvanian, and A. Sabzevari, “Intelligent vehicle routing for stochastic service times: a grouping evolution strategy approach,” International Journal of Transportation Science and Technology, 2025, doi: https://doi.org/10.1016/j.ijtst.2025.04.009.
F. Trigos and M. L. Osorio, “Integrating the Triple Bottom Line into the Vehicle Routing Problem: a transdisciplinary approach to customer prioritization,” Frontiers in Sustainability, vol. 6, 2025, doi: 10.3389/frsus.2025.1528514.
M. Wen, W. Sun, Y. Yu, J. Tang, and K. Ikou, “An adaptive large neighborhood search for the larger-scale multi depot green vehicle routing problem with time windows,” Journal of Cleaner Production, vol. 374, p. 133837, 2022, doi: 10.1016/j.jclepro.2022.133837.
X. Wang, W. Wang, and H. Zhang, “Collaborative multi-depot vehicle routing problem with dynamic customer demands and time windows,” Sustainability, vol. 14, no. 11, p. 6709, 2022, doi: 10.3390/su14116709.
J. Allen, “Vehicle routing problems for last mile distribution after major disaster,” Transportation Research Part E: Logistics and Transportation Review, vol. 95, pp. 1–12, 2017, doi: 10.1016/j.tre.2016.10.002.
P. H. V Penna, A. Subramanian, and L. S. Ochi, “An iterated local search heuristic for the multi-depot vehicle routing problem,” Journal of Heuristics, vol. 24, no. 3, pp. 197–232, 2018, doi: 10.1007/s10732-017-9342-0.
M. Alinaghian, M. Jamshidian, and E. B. Tirkolaee, “The time-dependent multi-depot fleet size and mix green vehicle routing problem: improved adaptive large neighbourhood search,” Optimization, vol. 71, no. 11, pp. 3165–3193, 2022, doi: 10.1080/02331934.2021.2018627.
Y. Song, Y. Liu, T. Liu, and R. Long, “Tabu search based on variable neighborhood and its application to multi-depot vehicle routing problem,” in Proceedings of SPIE - The International Society for Optical Engineering, 2023, p. 127023I. doi: 10.1117/12.2677693.
Y. Zhao, X. Shen, and Z. Ge, “A Knowledge-Guided Multi-Objective Shuffled Frog Leaping Algorithm for Dynamic Multi-Depot Multi-Trip Vehicle Routing Problem,” Symmetry, vol. 16, no. 6, p. 697, 2024, doi: 10.3390/sym16060697.
M. Saffarian, M. Niksirat, and S. M. Kazemi, “A hybrid genetic-simulated annealing-auction algorithm for a fully fuzzy multi-period multi-depot vehicle routing problem,” International Journal of Supply and Operations Management, vol. 8, no. 2, pp. 96–113, 2021, doi: 10.22034/IJSOM.2021.2.2.
A. Heakl, Y. S. Shaaban, M. Takac, S. Lahlou, and Z. Iklassov, “SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem,” 2025.
DOI: https://doi.org/10.47738/jads.v6i4.973
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0