EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 59 / No 6 (November-December 2025) RAIRO-Oper. Res., 59 6 (2025) 3703-3727 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 6, November-December 2025
Page(s) 3703 - 3727
DOI https://doi.org/10.1051/ro/2025117
Published online 19 December 2025
  • P. Abbasi Tavallali, M.R. Feylizadeh and A. Amindoust, Presenting a mathematical programming model for routing and scheduling of cross-dock and transportation in green reverse logistics network of covid-19 vaccines. Iran. J. Optim. 13, 2 (2011) 123–146. [Google Scholar]
  • Augerat, Capacitated VRP instances, 2021. Available at https://neo.lcc.uma.es/vrp/vrp-instances/capacitated-vrp-instances. [Google Scholar]
  • B. Balcik and B.M. Beamon, Facility location in humanitarian relief. Int. J. Logist. 11, 2 (2008) 101–121. [Google Scholar]
  • A.K. Biswal, M. Jenamani and S.K. Kumar, Warehouse efficiency improvement using rfid in a humanitarian supply chain: Implications for indian food security system. Transp. Res. E Logist. Transp. Rev. 109 (2018) 205–224. [Google Scholar]
  • J. Cai, Q. Zhu, Q. Lin, Z. Ming and K.C. Tan, Decomposition-based multiobjective evolutionary optimization with tabu search for dynamic pickup and delivery problems. IEEE Trans. Intel. Transp. Syst. 25 (2024) 14830–14843. [Google Scholar]
  • Y.S. Chang and H.J. Lee, Optimal delivery routing with wider drone-delivery areas along a shorter truck-route. Expert Syst. Appl. 104 (2018) 307–317. [Google Scholar]
  • M. Dorigo, V. Maniezzo and A. Colorni, Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man. Cybern. B Cybern. 26, 1 (1996) 29–41. [Google Scholar]
  • K. Dorling, J. Heinrichs, G.G. Messier and S. Magierowski, Vehicle routing problems for drone delivery. IEEE Trans. Syst. Man. Cybern. Syst. 47, 1 (2016) 70–85. [Google Scholar]
  • R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, 1995, pp. 39–43. [Google Scholar]
  • Y. Gao, H. Wu and W. Wang, A hybrid ant colony optimization with fireworks algorithm to solve capacitated vehicle routing problem. Appl. Intell. 53, 6 (2023) 7326–7342. [Google Scholar]
  • Z. Gao and C. Ye, Reverse logistics vehicle routing optimization problem based on multivehicle recycling. Mathematical Problems in Engineering 9 (2021) 5559684. [Google Scholar]
  • General Office of the State Council of the People’s Republic of China, The 13th five year plan for the construction of the national emergency response system [EB/OL], 2017. Available at http://www.gov.cn/zhengce/content/2017-07/19/content_5211752.htm. [Google Scholar]
  • V. Ghezavati and M. Beigi, Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure. J. Ind. Eng. Int. 12 (2016) 469–483. [Google Scholar]
  • F. Glover, Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13, 5 (1986) 533–549. [Google Scholar]
  • W. J. Gutjahr and S. Fischer, Equity and deprivation costs in humanitarian logistics. Eur. J. Oper. Res. 270, 1 (2018) 185–197. [Google Scholar]
  • W.J. Gutjahr and P.C. Nolz, Multicriteria optimization in humanitarian aid. Eur. J. Oper. Res. 252, 2 (2016) 351–366. [CrossRef] [Google Scholar]
  • J. Holguín-Veras, M. Jaller, L. N. Van Wassenhove, N. Pérez and T. Wachtendorf, On the unique features of post-disaster humanitarian logistics. J. Oper. Manag. 30, 7–8 (2012) 494–506. [Google Scholar]
  • J. Holguín-Veras, N. Pérez, M. Jaller, L. N. Van Wassenhove and F. Aros-Vera, On the appropriate objective function for post-disaster humanitarian logistics models. J. Oper. Manag. 31, 5 (2013) 262–280. [CrossRef] [Google Scholar]
  • J. H. Holland, Genetic algorithms and the optimal allocation of trials. SIAM J. Comput. 2, 2 (1973) 88–105. [Google Scholar]
  • M. Huang, K. Smilowitz and B. Balcik, Models for relief routing: Equity, efficiency and efficacy. Transp. Res. E Logist. Transp. Rev. 48, 1, SI (2012) 2–18. [Google Scholar]
  • İ. İlhan. An improved simulated annealing algorithm with crossover operator for capacitated vehicle routing problem. Swarm Evol. Comput. 64 (2021) 100911. [CrossRef] [Google Scholar]
  • H.Y. Jeong, B.D. Song and S. Lee, Truck-drone hybrid delivery routing: Payload-energy dependency and no-fly zones. Int. J. Prod. Econ. 214 (2019) 220–233. [CrossRef] [Google Scholar]
  • S. Karakatič, Optimizing nonlinear charging times of electric vehicle routing with genetic algorithm. Expert Syst. Appl. 164 (2021) 114039. [Google Scholar]
  • A. Kilic, M.C. Dincer and M.A. Gokce, Determining optimal treatment rate after a disaster. J. Oper. Res. Soc. 65, 7 (2014) 1053–1067. [Google Scholar]
  • W. Kim, J.-G. Kang and H. Kim. Pollution routing problem to reverse logistics of disposed food waste. Int. Inform. Institute Tokyo Inform. 19, 3 (2016) 771. [Google Scholar]
  • S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi, Optimization by simulated annealing. Science 220, 4598 (1983) 671–680. [CrossRef] [MathSciNet] [Google Scholar]
  • W. Li, L. Xia, Y. Huang and S. Mahmoodi, An ant colony optimization algorithm with adaptive greedy strategy to optimize path problems. J. Ambient Intell. Humaniz. Comput. 1 (2022) 1–15. [Google Scholar]
  • J.H. McCoy and H.L. Lee, Using fairness models to improve equity in health delivery fleet management. Prod. Oper. Manag. 23, 6 (2014) 965–977. [Google Scholar]
  • A. Moreno, D. Alem, M. Gendreau and P. Munari, The heterogeneous multicrew scheduling and routing problem in road restoration. Transp. Res. B Methodol. 141 (2020) 24–58. [Google Scholar]
  • C.C. Murray and A.G. Chu, The flying sidekick traveling salesman problem: optimization of drone-assisted parcel delivery. Transp. Res. Part C Emerg. Technol. 54 (2015) 86–109. [Google Scholar]
  • D.J. Nair, H. Grzybowska, Y. Fu and V.V. Dixit, Scheduling and routing models for food rescue and delivery operations. Socio-Econ. Plan. Sci. 63 (2018) 18–32. [Google Scholar]
  • M. Najafi, K. Eshghi and W. Dullaert, A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transp. Res. E Logist. Transp. Rev. 49, 1 (2013) 217–249. [Google Scholar]
  • N. Perez-Rodriguez and J. Holguin-Veras, Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transp. Sci. 50, 4 (2016) 1261–1285. [Google Scholar]
  • S. Poikonen and J.F. Campbell, Future directions in drone routing research. Networks 77, 1 (2021), 116–126. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Rabta, C. Wankmueller and G. Reiner, A drone fleet model for last-mile distribution in disaster relief operations. Int. J. Disaster Risk Reduct. 28 (2018) 107–112. [CrossRef] [Google Scholar]
  • O. Rodriguez-Espindola, P. Albores and C. Brewster, Dynamic formulation for humanitarian response operations incorporating multiple organisations. Int. J. Prod. Econ. 204 (2018) 83–98. [Google Scholar]
  • M. Salama and S. Srinivas, Joint optimization of customer location clustering and drone-based routing for last-mile deliveries. Transp. Res. C Emerg. Technol. 114 (2020) 620–642. [Google Scholar]
  • H. Soleimani, K. Govindan, H. Saghafi and H. Jafari, Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput. Ind. Eng. 109 (2017) 191–203. [Google Scholar]
  • N. Stojadinović, B. Bošković, D. Trifunović and S. Janković, Train path congestion management: Using hybrid auctions for decentralized railway capacity allocation. Transp. Res. A Policy Pract. 129 (2019) 123–139. [Google Scholar]
  • H. Sun, Y. Wang, and Y. Xue, A bi-objective robust optimization model for disaster response planning under uncertainties. Comput. Ind. Eng. 155 (2021) 107213. [CrossRef] [Google Scholar]
  • A.S. Tasan and M. Gen, A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries. Comput. Ind. Eng. 62, 3, SI (2012) 755–761. [Google Scholar]
  • B. Tessema and G. G. Yen, A self adaptive penalty function based algorithm for constrained optimization, in 2006 IEEE International Conference on Evolutionary Computation, IEEE, 2006, pp. 246–253. [Google Scholar]
  • X. Wang, X. Wang, L. Liang, X. Yue and L. N. Van Wassenhove, Estimation of deprivation level functions using a numerical rating scale. Prod. Oper. Manag. 26, 11 (2017) 2137–2150. [Google Scholar]
  • Z. Wang and J.-B. Sheu, Vehicle routing problem with drones. Transport. Res. B Methodol. 122 (2019) 350–364. [Google Scholar]
  • Y. Xiang and J. Zhuang, A medical resource allocation model for serving emergency victims with deteriorating health conditions. Ann. Oper. Res. 236, 1, SI (2016) 177–196. [Google Scholar]
  • Y. Xiao, Q. Zhao, I. Kaku and Y. Xu. Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput. Oper. Res. 39, 7 (2012) 1419–1431. [CrossRef] [MathSciNet] [Google Scholar]
  • L. Yu, H. Yang, L. Miao and C. Zhang, Rollout algorithms for resource allocation in humanitarian logistics. IISE Trans. 51, 8, SI (2019) 887–909. [Google Scholar]
  • L. Yu, C. Zhang, H. Yang and L. Miao, Novel methods for resource allocation in humanitarian logistics considering human suffering. Comput. Ind. Eng. 119 (2018) 1–20. [Google Scholar]
  • H. Zhang, H. Ge, J. Yang and Y. Tong, Review of vehicle routing problems: Models, classification and solving algorithms. Arch. Comput. Methods Eng. 29, 1 (2022) 195–221. [Google Scholar]
  • J. Zhang, F. Yang and X. Weng, An evolutionary scatter search particle swarm optimization algorithm for the vehicle routing problem with time windows. IEEE Access 6 (2018) 63468–63485. [Google Scholar]
  • L. Zhu, Y. Gong, Y. Xu and J. Gu, Emergency relief routing models for injured victims considering equity and priority. Ann. Oper. Res. 283 (2019) 1573–1606. [CrossRef] [MathSciNet] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.