Open Access
RAIRO-Oper. Res.
Volume 58, Number 2, March-April 2024
Page(s) 1979 - 1999
Published online 24 April 2024
  • G.B. Dantzig and J.H. Ramser, The truck dispatching problem. Manage. Sci. 6 (1959) 80–91. [Google Scholar]
  • B.Y. Ekren, S.K. Mangla, E.E. Turhanlar, Y. Kazancoglu and G. Li, Lateral inventory share-based models for IoT-enabled E-commerce sustainable food supply networks. Comput. Oper. Res. 130 (2021) 105237. [Google Scholar]
  • Z. Liu, Y. Zhang, M. Yu and X. Zhou, Heuristic algorithm for ready-mixed concrete plant scheduling with multiple mixers. Autom. Constr. 84 (2017) 1–13. [Google Scholar]
  • K.M. Ferreira, T.A. de Queiroz and F.M.B. Toledo, An exact approach for the green vehicle routing problem with two-dimensional loading constraints and split delivery. Comput. Oper. Res. 136 (2021) 105452. [Google Scholar]
  • J.K. Lenstra and A.H.G. Rinnooy Kan, Complexity of vehicle routing and scheduling problems. Networks 11 (1981) 221–227. [Google Scholar]
  • B. Kallehauge, N. Boland and O.B.G. Madsen, Path inequalities for the vehicle routing problem with time windows. Networks 49 (2007) 273–293. [Google Scholar]
  • R. Tao and C.-M. Tam, System reliability theory based multiple objective optimization model for construction projects. Autom. Constr. 31 (2013) 54–64. [Google Scholar]
  • N.N. Yan and D. Zheng, A study on the agent-based vehicles dispatching optimization at container terminals. Appl. Mech. Mater. 241–244 (2012) 1745–1750. [Google Scholar]
  • D.-Y. Lin and Y.-H. Ku, Using genetic algorithms to optimize stopping patterns for passenger rail transportation. Comput.-Aided Civil Infrastruct. Eng. 29 (2013) 264–278. [Google Scholar]
  • Q. Wang and C. Tang, Deep reinforcement learning for transportation network combinatorial optimization: a survey. Knowl.-Based Syst. 233 (2021) 107526. [Google Scholar]
  • R. Basso, B. Kulcsár and I. Sanchez-Diaz, Electric vehicle routing problem with machine learning for energy prediction. Transp. Res. Part B: Methodol. 145 (2021) 24–55. [Google Scholar]
  • K.-C. Ying and S.-W. Lin, Minimizing total completion time in the no-wait jobshop scheduling problem using a backtracking metaheuristic. Comput. Ind. Eng. 169 (2022) 108238. [Google Scholar]
  • W. Ongcunaruk, P. Ongkunaruk and G.K. Janssens, Genetic algorithm for a delivery problem with mixed time windows. Comput. Ind. Eng. 159 (2021) 107478. [Google Scholar]
  • L. Pasandi, M. Hooshmand and M. Rahbar, Modified A* Algorithm integrated with ant colony optimization for multi-objective route-finding; case study: Yazd. Appl. Soft Comput. 113 (2021) 107877. [Google Scholar]
  • A.M. Altabeeb, A.M. Mohsen, L. Abualigah and A. Ghallab, Solving capacitated vehicle routing problem using cooperative firefly algorithm. Appl. Soft Comput. 108 (2021) 107403. [Google Scholar]
  • Z.H. Ahmed and M. Yousefikhoshbakht, An improved tabu search algorithm for solving heterogeneous fixed fleet open vehicle routing problem with time windows. Alexandria Eng. J. 64 (2023) 349–363. [Google Scholar]
  • Y. Meliani, Y. Hani, S.L. Elhaq and A. El Mhamedi, A tabu search based approach for the heterogeneous fleet vehicle routing problem with three-dimensional loading constraints. Appl. Soft Comput. 126 (2022) 109239. [Google Scholar]
  • İ. İlhan, An improved simulated annealing algorithm with crossover operator for capacitated vehicle routing problem. Swarm Evol. Comput. 64 (2021) 100911. [Google Scholar]
  • M.M. Solomon, Algorithm for the vehicle routing and scheduling problems with time windows constraints. Oper. Res. 35 (1987) 254–265. [Google Scholar]
  • M.A. Masmoudi, S. Mancini, R. Baldacci and Y.-H. Kuo, Vehicle routing problems with drones equipped with multi-package payload compartments. Transp. Res. Part E: Logistics Transp. Rev. 164 (2022) 102757. [Google Scholar]
  • Y. Niu, D. Kong, R. Wen, Z. Cao and J. Xiao, An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand. Knowl.-Based Syst. 230 (2021) 107378. [Google Scholar]
  • A. Gutiérrez-Sánchez and L.B. Rocha-Medina, VRP variants applicable to collecting donations and similar problems: a taxonomic review. Comput. Ind. Eng. 164 (2022) 107887. [Google Scholar]
  • K.-W. Pang, An adaptive parallel route construction heuristic for the vehicle routing problem with time windows constraints. Expert Syst. App. 38 (2011) 11939–11946. [Google Scholar]
  • C. Chen, E. Demir and Y. Huang, An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots. Eur. J. Oper. Res. 294 (2021) 1164–1180. [Google Scholar]
  • A. Escudero-Santana, J. Muñuzuri, P. Cortés and L. Onieva, The one container drayage problem with soft time windows. Res. Transp. Econ. 90 (2021) 100884. [Google Scholar]
  • F. Errico, G. Desaulniers, M. Gendreau, W. Rei and L.-M. Rousseau, The vehicle routing problem with hard time windows and stochastic service times. Eur. J. Transp. Logistics 7 (2018) 223–251. [Google Scholar]
  • M. Gmira, M. Gendreau, A. Lodi and J.-Y. Potvin, Tabu search for the time-dependent vehicle routing problem with time windows on a road network. Eur. J. Oper. Res. 288 (2021) 129–140. [Google Scholar]
  • J.-F. Cordeau, G. Laporte and A. Mercier, A unified tabu search heuristic for vehicle routing problem with time window constraints. J. Oper. Res. Soc. 52 (2001) 928–936. [Google Scholar]
  • V.F. Yu, P. Jewpanya, A.A.N. Perwira Redi and Y.-C. Tsao, Adaptive neighborhood simulated annealing for the heterogeneous fleet vehicle routing problem with multiple cross-docks. Comput. Oper. Res. 129 (2021) 105205. [Google Scholar]
  • Y. Meliani, Y. Hani, S.L. Elhaq and A. El Mhamedi, A developed tabu search algorithm for heterogeneous fleet vehicle routing problem. IFAC-PapersOnLine 52 (2019) 1051–1056. [Google Scholar]

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