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Optimal integrated location and dispatching decisions for feeder bus route design problem

    Bo Sun Affiliation
    ; Ming Wei Affiliation
    ; Chunfeng Yang Affiliation

Abstract

Dispatch centres are an important part of the feeder bus network, and their location affects the design process of the feeder route. In some remote areas with weak transport infrastructure, it is very important to find an effective tool to simultaneously select the optimal location of the dispatch centre as well as transit routing process, which could improve the performance of the feeder bus system. The purpose of this article is to present an integrated optimization model for joint location and dispatching decisions for Feeder Bus Route Design (FBRD). The proposed methodology can select a number of best dispatch centres in alternative sets and calculate the order of the demand points visited by the feeder route. The objective of the model is to simultaneously minimize the total construction cost of selected dispatch centres and the total operational cost of the designed feeder bus system. The methodology facilitates obtaining solutions using the design of an improved double population Bacterial Foraging Optimization (BFO) algorithm. For example, it redefines the solution coding and the heuristic used to randomly initialize the initial population. When applied to the design of a feeder bus system for a station at Nanjing (China), the results reveal that a reduced budget may lead to change in the location of the dispatch centre; a more distant centre is required, which may increase the total mileage cost of all feeder routes. A detailed comparison of the improved and standard BFO and CPLEX shows that the difference between solutions is acceptable. However, the calculation time is greatly reduced, thus proving the effectiveness of the proposed algorithm.

Keyword : feeder bus route design, dispatch centre location, integrated optimization model, bacterial foraging optimization

How to Cite
Sun, B., Wei, M., & Yang, C. (2024). Optimal integrated location and dispatching decisions for feeder bus route design problem. Transport, 39(3), 240–249. https://doi.org/10.3846/transport.2024.20522
Published in Issue
Dec 17, 2024
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References

Calvete, H. I.; Galé, C.; Oliveros, M.-J.; Sánchez-Valverde, B. 2007. A goal programming approach to vehicle routing problems with soft time windows, European Journal of Operational Research 177(3): 1720–1733. https://doi.org/10.1016/j.ejor.2005.10.010

Capelle, T.; Cortés, C. E.; Gendreau, M.; Rey, P. A.; Rousseau, L.-M. 2019. A column generation approach for location-routing problems with pickup and delivery, European Journal of Operational Research 272(1): 121–131. https://doi.org/10.1016/j.ejor.2018.05.055

Christofides, N.; Beasley, J. E. 1984. The period routing problem, Networks 14(2): 237–256. https://doi.org/10.1002/net.3230140205

Ciaffi, F.; Cipriani, E.; Petrelli, M. 2012. Feeder bus network design problem: a new metaheuristic procedure and real size applications, Procedia – Social and Behavioral Sciences 54: 798–807. https://doi.org/10.1016/j.sbspro.2012.09.796

Deng, L.-B.; Gao, W.; Zhou, W.-L.; Lai, T. Z. 2013. Optimal design of feeder-bus network related to urban rail line based on transfer system, Procedia – Social and Behavioral Sciences 96: 2383–2394. https://doi.org/10.1016/j.sbspro.2013.08.267

Desaulniers, G.; Lavigne, J.; Soumis, F. 1998. Multi-depot vehicle scheduling problems with time windows and waiting costs, European Journal of Operational Research 111(3): 479–494. https://doi.org/10.1016/S0377-2217(97)00363-9

El-Sherbeny, N. A. 2010. Vehicle routing with time windows: an overview of exact, heuristic and metaheuristic methods, Journal of King Saud University – Science 22(3): 123–131. https://doi.org/10.1016/j.jksus.2010.03.002

Errico, F.; Crainic, T. G.; Malucelli, F.; Nonato, M. 2013. A survey on planning semi-flexible transit systems: methodological issues and a unifying framework, Transportation Research Part C: Emerging Technologies 36: 324–338. https://doi.org/10.1016/j.trc.2013.08.010

Kim, M.; Schonfeld, P. 2014. Integration of conventional and flexible bus services with timed transfers, Transportation Research Part B: Methodological 68: 76–97. https://doi.org/10.1016/j.trb.2014.05.017

Kohl, N.; Madsen, O. B. G. 1997. An optimization algorithm for the vehicle routing problem with time windows based on Lagrangian relaxation, Operations Research 45(3): 395–406. https://doi.org/10.1287/opre.45.3.395

Kuah, G. K.; Perl, J. 1989. The feeder-bus network-design problem, Journal of the Operational Research Society 40(8): 751–767. https://doi.org/10.1057/jors.1989.127

Li, X.; Wei, M.; Hu, J.; Yuan, Y.; Jiang, H. 2018. An agent-based model for dispatching real-time demand-responsive feeder bus, Mathematical Problems in Engineering 2018: 6925764. https://doi.org/10.1155/2018/6925764

Ma, C.; Hao, W.; He, R.; Jia, X.; Pan, F.; Fan, J.; Xiong, R. 2018. Distribution path robust optimization of electric vehicle with multiple distribution centres, Plos One 13(3): e0193789. https://doi.org/10.1371/journal.pone.0193789

Martins, C. L; Vaz Pato, M. V. 1998. Search strategies for the feeder bus network design problem, European Journal of Operational Research 106(2–3): 425–440. https://doi.org/10.1016/S0377-2217(97)00283-X

Mohaymany, A. S.; Gholami, A. 2010. Multimodal feeder network design problem: ant colony optimization approach, Journal of Transportation Engineering 136(4): 323–331. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000110

Pan, S.; Yu, J.; Yang, X.; Liu, Y.; Zou, N. 2015. Designing a flexible feeder transit system serving irregularly shaped and gated communities: determining service area and feeder route planning, Journal of Urban Planning and Development 141(3): 04014028. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000224

Qiu, F.; Li, W.; Haghani, A. 2015a. A methodology for choosing between fixed-route and flex-route policies for transit services, Journal of Advanced Transportation 49(3): 496–509. https://doi.org/10.1002/atr.1289

Qiu, F.; Li, W.; Haghani, A. 2015b. An exploration of the demand limit for flex-route as feeder transit services: a case study in Salt Lake City, Public Transport 7(2): 259–276. https://doi.org/10.1007/s12469-014-0097-9

Qiu, F.; Li, W.; Zhang, J. 2014. A dynamic station strategy to improve the performance of flex-route transit services, Transportation Research Part C: Emerging Technologies 48: 229–240. https://doi.org/10.1016/j.trc.2014.09.003

Quental, C.; Azevedo, M.; Azevedo, J.; Gonçalves, S. B.; Gonçalves, J. 2018. Influence of the musculotendon dynamics on the muscle force-sharing problem of the shoulder – a fully inverse dynamics approach, Journal of Biomechanical Engineering 140(7): 071005. https://doi.org/10.1115/1.4039675

Shen, J.; Yang, S.; Gao, X.; Qiu, F. 2017. Vehicle routing and scheduling of demand-responsive connector with on-demand stations, Advances in Mechanical Engineering 9(6): 1–10. https://doi.org/10.1177/1687814017706433

Solomon, M.; Chalifour, A.; Desrosiers, J.; Boisvert, J. 1992. An application of vehicle-routing methodology to large-scale larvicide control programs, Interfaces 22(3): 88–99. https://doi.org/10.1287/inte.22.3.88

Sun, B.; Wei, M.; Yang, C.; Xu, Z.; Wang, H. 2018a. Personalised and coordinated demand-responsive feeder transit service design: a genetic algorithms approach, Future Internet 10(7): 61. https://doi.org/10.3390/fi10070061

Sun, B.; Wei, M.; Zhu, S. 2018b. Optimal design of demand-responsive feeder transit services with passengers’ multiple time windows and satisfaction, Future Internet 10(3): 30. https://doi.org/10.3390/fi10030030

Tang, K.; Xiao, X; Wu, J.; Yang, J.; Luo, L. 2017. An improved multilevel thresholding approach based modified bacterial foraging optimization, Applied Intelligence 46(1): 214–226. https://doi.org/10.1007/s10489-016-0832-9

Wei, M; Chen, X.-W.; Sun, B. 2015. Model and algorithm of schedule coordination in regional bus transit with multiple transport modes, Journal of Highway and Transportation Research and Development (English Edition) 9(3): 78–84. https://doi.org/10.1061/JHTRCQ.0000460