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Best routes selection in multimodal networks using multi-objective genetic algorithm

Author

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  • Guiwu Xiong

    (Chongqing Key Laboratory of Logistics, Chongqing University)

  • Yong Wang

    (Chongqing Key Laboratory of Logistics, Chongqing University)

Abstract

In this study, we propose a bi-level multi-objective Taguchi genetic algorithm for a multimodal routing problem with time windows. The mathematic model is constructed, which is featured by two optimal objectives, multiple available transportation manners and different demanded delivery times. After thoroughly analyzing the characteristics of the formulated model, a corresponding bi-level multi-objective Taguchi genetic algorithm is designed to find the Pareto-optimal front. At the upper level, a genetic multi-objective algorithm simultaneously searches the Pareto-optimal front and provides the most feasible routing path choices for the lower level. After generalizing the matrices of costs and time in a multimodal transportation network, the $$k$$ -shortest path algorithm is applied to providing some potential feasible paths. A multi-objective genetic algorithm is proposed at the lower level to determine the local optimal combination of transportation manners for these potential feasible paths. To make the genetic algorithm more robust, sounder and faster, the Taguchi (orthogonal) experimental design method is adopted in generating the initial population and the crossover operator. The case study shows that the proposed algorithm can effectively find the Pareto-optimal front solutions and offer series of transportation routes with best combinations of transportation manners. The shipper can easily select the required shipping schemes with specified demands.

Suggested Citation

  • Guiwu Xiong & Yong Wang, 2014. "Best routes selection in multimodal networks using multi-objective genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 655-673, October.
    • Handle: RePEc:spr:jcomop:v:28:y:2014:i:3:d:10.1007_s10878-012-9574-8
    DOI: 10.1007/s10878-012-9574-8
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    References listed on IDEAS

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    1. Jean-Charles Créput & Amir Hajjam & Abderrafiaa Koukam & Olivier Kuhn, 2012. "Self-organizing maps in population based metaheuristic to the dynamic vehicle routing problem," Journal of Combinatorial Optimization, Springer, vol. 24(4), pages 437-458, November.
    2. Niaz A. Wassan & A. Hameed Wassan & Gábor Nagy, 2008. "A reactive tabu search algorithm for the vehicle routing problem with simultaneous pickups and deliveries," Journal of Combinatorial Optimization, Springer, vol. 15(4), pages 368-386, May.
    3. Guiwu Xiong & Yong Wang, 2010. "Research on job integration of multi-agent in multimodal transportation with time windows," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 2(3/4), pages 307-321.
    4. Wanpracha Chaovalitwongse & Dukwon Kim & Panos M. Pardalos, 2003. "GRASP with a New Local Search Scheme for Vehicle Routing Problems with Time Windows," Journal of Combinatorial Optimization, Springer, vol. 7(2), pages 179-207, June.
    5. R. Montemanni & L. M. Gambardella & A. E. Rizzoli & A. V. Donati, 2005. "Ant Colony System for a Dynamic Vehicle Routing Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 327-343, December.
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    Cited by:

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    3. Chunjiao Shao & Haiyan Wang & Meng Yu, 2022. "Multi-Objective Optimization of Customer-Centered Intermodal Freight Routing Problem Based on the Combination of DRSA and NSGA-III," Sustainability, MDPI, vol. 14(5), pages 1-25, March.
    4. Li, Zhaojin & Liu, Ya & Yang, Zhen, 2021. "An effective kernel search and dynamic programming hybrid heuristic for a multimodal transportation planning problem with order consolidation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    5. Xue Li & Zhengwen He & Nengmin Wang & Mario Vanhoucke, 2022. "Multimode time-cost-robustness trade-off project scheduling problem under uncertainty," Journal of Combinatorial Optimization, Springer, vol. 43(5), pages 1173-1202, July.
    6. Majbah Uddin & Nathan Huynh, 2019. "Reliable Routing of Road-Rail Intermodal Freight under Uncertainty," Networks and Spatial Economics, Springer, vol. 19(3), pages 929-952, September.
    7. Yan Sun & Xinya Li & Xia Liang & Cevin Zhang, 2019. "A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability," Sustainability, MDPI, vol. 11(9), pages 1-27, May.
    8. Zhiguo Wang & Lufei Huang & Cici Xiao He, 0. "A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-28.
    9. Dandan Chen & Yong Zhang & Liangpeng Gao & Russell G. Thompson, 2019. "Optimizing Multimodal Transportation Routes Considering Container Use," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    10. Yan Sun & Xinya Li, 2019. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncerta," Mathematics, MDPI, vol. 7(8), pages 1-40, August.
    11. Archetti, Claudia & Peirano, Lorenzo & Speranza, M. Grazia, 2022. "Optimization in multimodal freight transportation problems: A Survey," European Journal of Operational Research, Elsevier, vol. 299(1), pages 1-20.

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