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All issues Volume 60 / No 1 (January-February 2026) RAIRO-Oper. Res., 60 1 (2026) 407-435 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 60, Number 1, January-February 2026
Page(s) 407 - 435
DOI https://doi.org/10.1051/ro/2026002
Published online 06 March 2026
  • S. Lin and B.W. Kernighan, An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21 (1973) 498–516. [Google Scholar]
  • M.I. Hosny, Inves heur and meta-heuristic algorithms for solving pickup and delivery problems. Ph.D. thesis, Science Informatics, Cardiff University, Cardiff (2010). [Google Scholar]
  • K. Braekers, K. Ramaekers and I. Van Nieuwenhuyse. The vehicle routing problem: state of the art classification and review. Comput. Ind. Eng. 99 (2016) 300–313. [Google Scholar]
  • C. Beneich and S.M. Douiri, Dynamic multi-compartment vehicle routing problem: formulation and algorithm, in Artificial Intelligence, Data Science and Applications, edited by Y. Farhaoui, A. Hussain, T. Saba, H. Taherdoost and A. Verma. Springer Nature Switzerland, Cham (2024) 100–105. [Google Scholar]
  • H. Psaraftis, Dynamic vehicle routing problems, in Vehicle Routing: Methods and Studies. North-Holland (1988) 223–248. [Google Scholar]
  • V. Pillac, M. Gendreau, C. Guéret and A.L. Medaglia, A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225 (2013) 1–11. [Google Scholar]
  • G. Berbeglia, J.-F. Cordeau and G. Laporte, Dynamic pickup and delivery problems. Eur. J. Oper. Res. 202 (2010) 8–15. [Google Scholar]
  • D.J. Bertsimas and G. van Ryzin, A stochastic and dynamic vehicle routing problem in the Euclidean plane. Oper. Res. 39 (1991) 601–615. [Google Scholar]
  • N. Ascheuer, S.O. Krumke and J. Rambau, Online dial-a-ride problems: minimizing the completion time, in STACS 2000, edited by H. Reichel and S. Tison. Springer Berlin Heidelberg, Berlin, Heidelberg (2000) 639–650. [Google Scholar]
  • D.B. Shmoys, J. Wein and D.P. Williamson, Scheduling parallel machines on-line. SIAM J. Comput. 24 (1995) 1313–1331. [Google Scholar]
  • A. Karlin, M. Manasse, L. Rudolph and D. Sleator, Competitive snoopy paging. Algorithmica 3 (1988) 70–119. [Google Scholar]
  • D. Sleator and R. Tarjan, Amortized efficiency of list update paging rules. Commun. ACM 28 (1985) 202–208. [Google Scholar]
  • A. Borodin and R. El-Yaniv, Online Computation and Competitive Analysis. Cambridge University Press (2005). [Google Scholar]
  • S. Ichoua, M. Gendreau and J.-Y. Potvin, Diversion issues in real-time vehicle dispatching. Transp. Sci. 34 (2000) 426–438. [Google Scholar]
  • M. Gendreau, F. Guertin, J.-Y. Potvin and E. Taillard, Parallel tabu search for real-time vehicle routing and dispatching. Transp. Sci. 33 (1999) 381–390. [Google Scholar]
  • J. Yang, P. Jaillet and H. Mahmassani, Real-time multivehicle truckload pickup and delivery problems. Transp. Sci. 38 (2004) 135–148. [Google Scholar]
  • A. Larsen, O. Madsen and M. Solomon, Partially dynamic vehicle routing models and algorithms. J. Oper. Res. Soc. 53 (2002) 637–646. [Google Scholar]
  • M. Ghannam and A. Gleixner, Hybrid genetic search for dynamic vehicle routing with time windows, in International Conference on Operations Research. Springer Nature Switzerland, Cham (2023) 273–280. [Google Scholar]
  • B. Herdianto, R. Billot, F. Lucas and M. Sevaux, Metaheuristic enhanced with feature-based guidance and diversity management for solving the capacitated vehicle routing problem. arXiv: 2407.20777 (2024). [Google Scholar]
  • J.F. Sze, S. Salhi and N. Wassan, An adaptive variable neighbourhood search approach for the dynamic vehicle routing problem. Comput. Oper. Res. 164 (2024) 106531. [Google Scholar]
  • K. Danach, L. Saker and H. Harb, Integrating metaheuristics and machine learning for enhanced vehicle routing: a comparative study of hyperheuristic and VAE based approaches. World Electr. Veh. J. 16 (2025) 258. [Google Scholar]
  • G. Ghiani, F. Guerriero, G. Laporte and R. Musmanno, Real-time vehicle routing: solution concepts, algorithms and parallel computing strategies. Eur. J. Oper. Res. 151 (2003) 1–11. [Google Scholar]
  • J.-J. Jaw, A.R. Odoni, H.N. Psaraftis and N.H.M. Wilson, A heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows. Transp. Res. Part B: Methodol. 20 (1986) 243–257. [Google Scholar]
  • O. Madsen, H. Ravn and J. Rygaard, A heuristic algorithm for a dial-a-ride problem with time windows, multiple capacities, and multiple objectives. Ann. Oper. Res. 60 (1995) 193–208. [Google Scholar]
  • D.J. Bertsimas and G. van Ryzin, A stochastic and dynamic vehicle routing problem in the Euclidean plane. Oper. Res. 39 (1991) 601–615. [Google Scholar]
  • R.W. Calvo, A.E. Fallahi and C. Prins, A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem. Comput. Oper. Res. 35 (2008) 1725–1741. [Google Scholar]
  • G. Pang and L. Muyldermans, On the benefits of co-collection: experiments with a multi-compartment vehicle routing algorithm. Eur. J. Oper. Res. 206 (2010) 93–103. [Google Scholar]
  • R. Evering, M. Reed and A. Yiannakou, An ant colony algorithm for the multicompartment vehicle routing problem. Appl. Soft Comput. 15 (2014) 169–176. [Google Scholar]
  • M. Ritt and P.V. Silvestrin, An iterated tabu search for the multi-compartment vehicle routing problem. Comput. Oper. Res. 81 (2017) 192–202. [Google Scholar]
  • G. Laporte and L.C. Coelho, Classification, models and exact algorithms for multicompartment delivery problems. Eur. J. Oper. Res. 242 (2015) 854–864. [Google Scholar]
  • G. Laporte, R. Lahyani and L.C. Coelho, A multicompartment vehicle routing problem arising in the collection of olive oil in Tunisia. Omega 51 (2015) 1–10. [Google Scholar]
  • M. Alinaghian, and N. Shokouhi, Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search. Omega 76 (2018) 85–99. [CrossRef] [Google Scholar]
  • S. Martins, M. Ostermeier, P. Amorim, A. Hübner and B. Almada-Lobo, Product-oriented time window assignment for a multi-compartment vehicle routing problem. Eur. J. Oper. Res. 276 (2019) 893–909. [Google Scholar]
  • J. Chen and J. Shi, A multi-compartment vehicle routing problem with time windows for urban distribution a comparison study on particle swarm optimization algorithms. Comput. Ind. Eng. 133 (2019) 95–106. [Google Scholar]
  • C. Beneich and S. Douiri, Solving the multi compartment vehicle routing problem using a hybridized simulated annealing algorithm. Int. J. Appl. Comput. Math. 9 (2023) 10. [Google Scholar]
  • 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 84 (2024) 300–325. [Google Scholar]
  • G. Baptista, M. Vieira and T. Pinto, An exact approach to the multi-compartment vehicle routing problem: the case of a fuel distribution company. Mathematics 12 (2024) 527. [Google Scholar]
  • J. Hu and C. Wang, A deep reinforcement-learning based route optimization model for multi-compartment cold chain distribution. Mathematics 13 (2025) 2039. [Google Scholar]
  • J.E. Mendoza, B. Castanier, C. Guéret, A.L. Medaglia and N. Velasco, A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Comput. Oper. Res. 37 (2010) 1886–1898. [Google Scholar]
  • A. Løkketangen and J. Oppen, A tabu search approach for the livestock collection problem. Oper. Res. 35 (2008) 3213–3229. [Google Scholar]
  • C. Archetti, N. Bianchessi and M. Grazia Speranza, Branch-and-cut algorithms for the split delivery vehicle routing problem. Eur. J. Oper. Res. 238 (2014) 685–698. [Google Scholar]
  • M. Ostermeier and A. Hübner, Vehicle selection for a multi-compartment vehicle routing problem. Eur. J. Oper. Res. 269 (2018) 682–694. [Google Scholar]
  • A. Sforza, P. Avella and M. Boccia, Solving a fuel delivery problem by heuristic and exact approaches. Eur. J. Oper. Res 152 (2004) 170–179. [Google Scholar]
  • F. Cornillier, F. Boctor, G. Laporte and J. Renaud, An exact algorithm for the petrol station replenishment problem. J. Oper. Res. Soc. 59 (2008) 607–615. [Google Scholar]
  • M. Mavrovouniotis and S. Yang, Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Inf. Sci. 294 (2015) 456–477. [Google Scholar]
  • J. Branke, Memory enhanced evolutionary algorithms for changing optimization problems, in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406). Vol. 3. (1999) 1875–1882. [Google Scholar]
  • T. Nguyen and X. Yao, Dynamic time-linkage problems revisited, in Workshops on Applications of Evolutionary Computation. Springer Berlin Heidelberg, Berlin, Heidelberg (2009) 735–744. [Google Scholar]
  • M. Mavrovouniotis, S. Yang and X. Yao, A benchmark generator for dynamic permutation-encoded problems, in Parallel Problem Solving from Nature PPSN XII, edited by C.A. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia and M. Pavone. Springer Berlin Heidelberg, Berlin, Heidelberg (2012) 508–517. [Google Scholar]
  • S. Kirkpatrick, C.D. Gelatt, Jr. and M.P. Vecchi, Optimization by simulated annealing. Science 220 (1983) 671–680. [CrossRef] [MathSciNet] [Google Scholar]
  • C. Prins, A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31 (2004) 1985–2002. [Google Scholar]
  • N. Christofides and S. Eilon, An algorithm for the vehicle-dispatching problem. OR 20 (1969) 309–318. [Google Scholar]
  • E. Taillard, Parallel iterative search methods for vehicle routing problems. Networks 23 (1993) 661–673. [Google Scholar]

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