The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach
RAIRO. Operations Research, Tome 55 (2021), pp. S3035-S3050

The uncertainty of transportation duration between nodes is an inherent characteristic and should be concerned in the routing optimization of the multimodal transportation network to guarantee the reliability of delivery time. The interval number is used to deal with the uncertainty of transportation duration, and the multi-objective robust optimization model is established which covers the transportation duration and the cost. To solve the combinatorial optimization problem of this study, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is designed, which integrates the (μ + λ) selection method elite retention and the external filing elite retention. Our findings verify the efficiency of the proposed approach by analyzing the diversity, distribution and convergence of the frontier solutions. Finally, near-optimal solutions are obtained with the proposed algorithm in the numerical example. The present study can provide decision reference for multimodal transportation carriers in making transportation plan under uncertainty.

DOI : 10.1051/ro/2020110
Classification : 90B06
Keywords: Multimodal transportation, multi-objective, route optimization, uncertainty, NSGA-II
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     title = {The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach},
     journal = {RAIRO. Operations Research},
     pages = {S3035--S3050},
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     publisher = {EDP-Sciences},
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     url = {https://www.numdam.org/articles/10.1051/ro/2020110/}
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Peng, Yong; Yong, Pengcheng; Luo, Yijuan. The route problem of multimodal transportation with timetable under uncertainty: multi-objective robust optimization model and heuristic approach. RAIRO. Operations Research, Tome 55 (2021), pp. S3035-S3050. doi: 10.1051/ro/2020110

[1] A. Abbassi, E. H. A. Ahmed and J. Boukachour, Robust optimisation of the intermodal freight transport problem: modeling and solving with an efficient hybrid approach. J. Comput. Sci. 30 (2019) 127–142. | DOI

[2] G. Assadipour, G. Y. Ke and M. Verma, Planning and managing intermodal transportation of hazardous materials with capacity selection and congestion. Transp. Res. Part E: Logistics Transp. Rev. 76 (2015) 45–57. | DOI

[3] H. Ayed, Z. Habbas, D. Khadraoui and F. Galvez, A parallel algorithm for solving time dependent multimodal transport problem [C]. In: International IEEE Conference on Intelligent Transportation Systems. IEEE, Washington, DC (2011).

[4] F. Bruns and S. Knust, Optimized load planning of trains in intermodal transportation. OR Spectrum 34 (2012) 511–533. | MR | Zbl | DOI

[5] T. S. Chang, Best routes selection in international intermodal networks. J. Dalian Maritime Univ. 35 (2008) 2877–2891. | Zbl

[6] J. H. Cho, H. S. Kim, H. R. Choi, N. K. Park and M. H. Kang, An intermodal transport network planning algorithm using dynamic programming. Appl. Intell. 36 (2012) 529–541. | DOI

[7] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2002) 182–197. | DOI

[8] E. Demir, W. Burgholzer, M. Hrušovský, E. Arıkan, W. Jammernegg and T. Van Woensel, A green intermodal service network design problem with travel time uncertainty. Transp. Res. Part B: Methodol. 93 (2016) 789–807. | DOI

[9] 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. | DOI

[10] L. J. Fogel, A. J. Owens and M. J. Walsh, Artificial Intelligence Through Simulated Evolution. John Wiley & Sons, New York, NY (1966). | Zbl

[11] A. Ghaderi and R. L. Burdett, An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network. Transp. Res. Part E: Logistics Transp. Rev. 127 (2019) 49–65. | DOI

[12] S. E. Grasman, Dynamic approach to strategic and operational multimodal routing decisions. Int. J. Logistics Syst. Manage. 2 (2006) 96. | DOI

[13] C. Hao and Y. Yue, Optimization on combination of transport routes and modes on dynamic programming for a container multimodal transport system. Proc. Eng. 137 (2016) 382–390. | DOI

[14] M. Hrušovský, E. Demir, W. Jammernegg and T. Van Woensel, Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty. Flexible Serv. Manuf. J. 30 (2016) 486–516. | DOI

[15] 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. | DOI

[16] 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. | DOI

[17] S. Liu, Y. Peng, Q. K. Song and Y. Y. Zhong, The robust shortest path problem for multimodal transportation considering timetable with interval data. Syst. Sci. Control Eng. 6 (2018) 68–78. | DOI

[18] C. Macharis and E. Pekin, Assessing policy measures for the stimulation of intermodal transport: a GIS-based policy analysis. J. Transp. Geogr. 17 (2009) 500–508. | DOI

[19] I. A. Martínez-Salazar, J. Molina, F. Ángel-Bello, T. Gómez and R. Caballero, Solving a bi-objective transportation location routing problem by metaheuristic algorithms. Eur. J. Oper. Res. 234 (2014) 25–36. | MR | Zbl | DOI

[20] 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. | MR | Zbl

[21] M. Mnif and S. Bouamama, Firework algorithm for multi-objective optimization of a multimodal transportation network problem. Proc. Comput. Sci. 112 (2017) 1670–1682. | DOI

[22] R. E. Moore, Methods and Applications of Interval Analysis. Vol 2 of Studies in Applied and Numerical Mathematics. SIAM, Philadelphia, PA (1979). | MR | Zbl

[23] 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. | DOI

[24] J. R. Schott, Fault tolerant design using single and multi-criteria genetic algorithms. Masters Thesis, Massachusetts Institute of Technology (1995).

[25] Y. Sheng and Y. Gao, Shortest path problem of uncertain random network. Comput. Ind. Eng. 99 (2016) 97–105. | DOI

[26] M. Steadieseifi, N. P. Dellaert, W. Nuijten, T. Van Woensel and R. Raoufi, Multimodal freight transportation planning: a literature review. Eur. J. Oper. Res. 233 (2014) 1–15. | DOI

[27] J. C. Thill and H. Lim, Intermodal containerized shipping in foreign trade and regional accessibility advantages. J. Transp. Geogr. 18 (2010) 530–547. | DOI

[28] 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. | DOI

[29] 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. | MR | DOI

[30] 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. | DOI

[31] H. Wei and M. Dong, Import-export freight organization and optimization in the dry-port-based cross-border logistics network under the Belt and Road Initiative. Comput. Ind. Eng. 130 (2019) 472–484. | DOI

[32] 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. | DOI

[33] S. Yan, L. Maoxiang and W. Danzhu, Bi-objective modelling for hazardous materials road-rail multimodal routing problem with railway schedule-based space-time constraints. Int. J. Environ. Res. Public Health 13 (2016) 1–31.

[34] X. Yang, J. M. W. Low and L. C. Tang, Analysis of intermodal freight from China to Indian Ocean: a goal programming approach. J. Transp. Geogr. 19 (2011) 515–527. | DOI

[35] K. Yang, L. Yang and Z. Gao, Planning and optimization of intermodal hub-and-spoke network under mixed uncertainty. Transp. Res. Part E: Logistics Transp. Rev. 95 (2016) 248–266. | DOI

[36] M. H. F. Zarandi, A. Hemmati, S. Davari and I. B. Turksen, Capacitated location-routing problem with time windows under uncertainty. Knowl.-Based Syst. 37 (2013) 480–489. | DOI

[37] 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. | DOI

[38] J. Zhang, Q. Zhang and L. Zhang, A Study on the Paths Choice of Intermodal Transport Based on Reliability, in Z. Zhang, Z. Shen, J. Zhang, R. Zhang (eds) LISS 2014. Springer, Berlin-Heidelberg (2015) 305–315.

[39] Y. Zhang, P. Liu, L. Yang and Y. Gao, A bi-objective model for uncertain multi-modal shortest path problems. J. Uncertainty Anal. App. 3 (2015) 8. | DOI

[40] E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3 (1999) 257–271. | DOI

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