Open Access
Issue
RAIRO-Oper. Res.
Volume 57, Number 4, July-August 2023
Page(s) 1745 - 1765
DOI https://doi.org/10.1051/ro/2023090
Published online 11 July 2023
  • T.G. Crainic and K.H. Kim, Intermodal transportation. Handb. Oper. Res. Manage. Sci. 14 (2007) 467–537. [Google Scholar]
  • Z. He, K. Navneet, W. van Dam and P. Van Mieghem, Robustness assessment of multimodal freight transport networks. Reliab. Eng. Syst. Saf. 207 (2021) 107315. [CrossRef] [Google Scholar]
  • W. Zhu, H. Wang and X. Zhang, Synergy evaluation model of container multimodal transport based on BP neural network. Neural Comput. App. 33 (2021) 4087–4095. [CrossRef] [Google Scholar]
  • T. Yamada, B.F. Russ, J. Castro and E. Taniguchi, Designing multimodal freight transport networks: a heuristic approach and applications. Transp. Sci. 43 (2009) 129–143. [CrossRef] [Google Scholar]
  • H.G. Resat and M. Turkay, A discrete-continuous optimization approach for the design and operation of synchromodal transportation networks. Comput. Ind. Eng. 130 (2019) 512–525. [CrossRef] [Google Scholar]
  • W. Qu, J. Rezaei, Y. Maknoon and L. Tavasszy, Hinterland freight transportation replanning model under the framework of synchromodality. Transp. Res. 131 (2019) 308–328. [Google Scholar]
  • Y. Sheng and Y. Gao, Shortest path problem of uncertain random network. Comput. Ind. Eng. 99 (2016) 97–105. [CrossRef] [Google Scholar]
  • L. Wang, L. Yang and Z.Y. Gao, The constrained shortest path problem with stochastic correlated link travel times. Eur. J. Oper. Res. 255 (2016) 43–57. [CrossRef] [Google Scholar]
  • X. Wang and Q. Meng, Discrete intermodal freight transportation network design with route choice behavior of intermodal operators. Transp. Res. Part B Methodol. 95 (2017) 76–104. [CrossRef] [Google Scholar]
  • R. Zhang, W.Y. Yun and I.K. Moon, Modeling and optimization of a container drayage problem with resource constraints. Int. J. Prod. Econ. 133 (2011) 351–359. [CrossRef] [Google Scholar]
  • Y. Zhang, B. Atasoy and R.R. Negenborn, Preference-based multi-objective optimization for synchromodal transport using adaptive large neighborhood search. Transp. Res. Record 2676 (2022) 71–87. [CrossRef] [Google Scholar]
  • H.G. Resat and M. Turkay, Design and operation of intermodal transportation network in the Marmara region of Turkey. Transp. Res. Part E: Logistics Transp. Rev. 83 (2015) 16–33. [CrossRef] [Google Scholar]
  • J.S.L. Lam and Y. Gu, A market-oriented approach for intermodal network optimization meeting cost, time and environmental requirements. Int. J. Prod. Econ. 171 (2016) 266–274. [CrossRef] [Google Scholar]
  • H. Wei and M. Dong, Import-export freight organization and optimization in the dry-portbased cross-border logistics network under the Belt and Road Initiative. Comput. Ind. Eng. 130 (2019) 472–484. [CrossRef] [Google Scholar]
  • G. Mavrotas and K. Florios, An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Appl. Math. Comput. 219 (2013) 9652–9669. [MathSciNet] [Google Scholar]
  • M. Kalinina, L. Olsson and A. Larsson, A multi objective chance constrained programming model for intermodal logistics with uncertain time. Int. J. Comput. Sci. 10 (2013) 35–44. [Google Scholar]
  • L. Li, R.R. Negenborn and B. De Schutter, Intermodal freight transport planning: a receding horizon control approach. Transp. Res. Part C: Emerg. Technol. 60 (2015) 77–95. [CrossRef] [Google Scholar]
  • Y. Sun, M. Lang and D. Wang, Bi-objective modelling for hazardous materials road-rail multimodal routing problem with railway schedule-based space-time constraints. Int. J. Environ. Res. Publ. Health 13 (2016) 762. [CrossRef] [Google Scholar]
  • S. Liu, Y. Peng, Q. Song and Y. Zhong, The robust shortest path problem for multimodal transportation considering timetable with interval data. Syst. Sci. Control Eng. 6 (2018) 68–78. [Google Scholar]
  • H. Zhang, Y. Li, Q. Zhang and D. Chen, Route selection of multimodal transport based on china railway transportation. J. Adv. Transp. 2021 (2021) 9984659. [Google Scholar]
  • R. Wang, K. Yang, L. Yang and Z.Y. Gao, Modeling and optimization of a road-rail intermodal transport system under uncertain information. Eng. App. Artif. Intell. 72 (2018) 423–436. [CrossRef] [Google Scholar]
  • A. Abbassi, E.H.A. Ahmed and J. Boukachour, Robust optimization of the intermodal freight transport problem: modeling and solving with an efficient. J. Comput. Sci. 30 (2019) 127–142. [CrossRef] [Google Scholar]
  • Y. Lu, M. Lang, Y. Sun and S. Li, A fuzzy intercontinental road-rail multimodal routing model with time and train capacity uncertainty and fuzzy programming approaches. IEEE Access 8 (2020) 27532–27548. [CrossRef] [Google Scholar]
  • E. Demir, W. Burgholzer, M. Hrusovsky, E. Arkan, W. Jammernegg and T.V. Woensel, A green intermodal service network design problem with travel time uncertainty. Transp. Res. Part B: Methodol. 93 (2016) 789–807. [CrossRef] [Google Scholar]
  • S. Fazayeli, A. Eydi and I.N. Kamalabadi, Location-routing problem in multimodal transportation network with time windows and fuzzy demands: presenting a two-part genetic algorithm. Comput. Ind. Eng. 119 (2018) 233–246. [CrossRef] [Google Scholar]
  • Z. Ziaei and A. Jabbarzadeh, A multi-objective robust optimization approach for green location-routing planning of multi-modal transportation systems under uncertainty. J. Cleaner Prod. 291 (2020) 125–293. [Google Scholar]
  • P. Robbe, D. Nuyens and S. Vandewalle, Recycling samples in the multigrid multilevel (quasi-) Monte Carlo method. SIAM J. Sci. Comput. 4541 (2019) S37–S60. [CrossRef] [Google Scholar]
  • J. Guo, Q. Du and Z. He, A method to improve the resilience of multimodal transport network: location selection strategy of emergency rescue facilities. Comput. Ind. Eng. 161 (2021) 107678. [CrossRef] [Google Scholar]
  • C. Cintrano, F. Chicano and E. Alba, Facing robustness as a multi-objective problem: a biobjective shortest path problem in smart regions. Inf. Sci. 503 (2019) 255–273. [CrossRef] [Google Scholar]
  • H. Wang, L. Tan, J. Shi, X. Lv and X. Lian, An improved NSGA-II algorithm for UAV path planning problems. J. Internet Technol. 22 (2021) 583–592. [Google Scholar]
  • Z. Li, Y. Liu and Z. Yang, An effective kernel search and dynamic programming hybrid heuristic for a multimodal transportation planning problem with order consolidation. Transp. Res. Part E: Logistics Transp. Rev. 152 (2021) 102408. [CrossRef] [Google Scholar]
  • Y. Peng, P. Yong and Y. Luo, The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach. RAIRO: Oper. Res. 55 (2021) S3035–S3050. [CrossRef] [EDP Sciences] [Google Scholar]
  • X. Chen and Y. Dai, Research on an improved ant colony algorithm fusion with genetic algorithm for route planning, in IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Vol. 1. IEEE (2020) 1273–1278. [Google Scholar]
  • G. Zhang, H. Wang, W. Zhao, Z. Guan and P. Li, Application of improved multi-objective ant colony optimization algorithm in ship weather routing. J. Ocean Univ. Chin. 20 (2021) 45–55. [CrossRef] [Google Scholar]
  • F. Xie, J. Long, Z. Qian, Z. Ding and L. Liu, Multi-objective optimization routing for satellite network based on ant colony algorithm, in IEEE 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE (2021) 353–356. [Google Scholar]
  • H. Wang, Y. Jin, C. Sun and J. Doherty, Offline data-driven evolutionary optimization using selective surrogate ensembles. IEEE Trans. Evol. Comput. 23 (2018) 203–216. [Google Scholar]
  • X. Chao, Y. Zhongqing, L. Jinhua and Y. Xixin, Application of data driven technology in wastewater treatment process, in IEEE 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT). IEEE (2020) 48–55. [Google Scholar]
  • Q. Gu, D. Wang, S. Jiang, N. Xiong and Y. Jin, An improved assisted evolutionary algorithm for data-driven mixed integer optimization based on Two_Arch. Comput. Ind. Eng. 159 (2021) 107463. [CrossRef] [Google Scholar]
  • H. Wang and Y. Jin, A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems. IEEE Trans. Cybern. 50 (2018) 536–549. [Google Scholar]
  • H. Lou, B. Gao, F. Jin, Y. Wan and Y. Wang, Shear wall layout optimization strategy for high-rise buildings based on conceptual design and data-driven tabu search. Comput. Struct. 250 (2021) 106546. [CrossRef] [Google Scholar]
  • Y. Peng, Y.J. Luo, P. Jiang and P.C. Yong, The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach. Eng. Comput. 39 (2022) 587–608. [CrossRef] [Google Scholar]
  • A. Juan, J. Faulin, S. Grasman, D. Riera, J. Marull and C. Mendez, Using safety stocks and simulation to solve the vehicle routing problem with stochastic demands. Transp. Res. Part C: Emerg. Technol. 19 (2011) 751–765. [CrossRef] [Google Scholar]
  • M. Dorigo and L.M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1 (1997) 53–66. [Google Scholar]
  • Z.Y. Duan, Z.X. Lei and S. Sun, Multi-objective robust optimisation method for stochastic time-dependent vehicle routing problem. J. Southwest Jiaotong Univ. 54 (2019) 565–572. [Google Scholar]
  • V. Vapnik, The Nature of Statistical Learning Theory. Springer Science & Business Media (2013). [Google Scholar]
  • M.S. Ahmad, S.M. Adnan, S. Zaidi and P. Bhargava, A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens. Constr. Build. Mater. 248 (2020) 118475. [CrossRef] [Google Scholar]
  • Y. Zhou, T. Kundu, M. Goh and J.B. Sheu, Multimodal transportation network centrality analysis for belt and road initiative. Transp. Res. Part E: Logistics Transp. Rev. 149 (2021) 102292. [CrossRef] [Google Scholar]

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