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Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications

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  • Rinaldi, Marco
  • Picarelli, Erika
  • D'Ariano, Andrea
  • Viti, Francesco

Abstract

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed Integer Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxembourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.

Suggested Citation

  • Rinaldi, Marco & Picarelli, Erika & D'Ariano, Andrea & Viti, Francesco, 2020. "Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications," Omega, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:jomega:v:96:y:2020:i:c:s0305048318314075
    DOI: 10.1016/j.omega.2019.05.006
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    5. Gui, Dongdong & Le, Meilong & Huang, Zhouchun & Zhang, Junfeng & D’Ariano, Andrea, 2023. "Optimal aircraft arrival scheduling with continuous descent operations in busy terminal maneuvering areas," Journal of Air Transport Management, Elsevier, vol. 107(C).
    6. Kouridis, Ch & Vlachokostas, Ch, 2022. "Towards decarbonizing road transport: Environmental and social benefit of vehicle fleet electrification in urban areas of Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    7. Chen, Zebin & Li, Shukai & D’Ariano, Andrea & Yang, Lixing, 2022. "Real-time optimization for train regulation and stop-skipping adjustment strategy of urban rail transit lines," Omega, Elsevier, vol. 110(C).
    8. Zhou, Yu & Wang, Hua & Wang, Yun & Yu, Bin & Tang, Tianpei, 2024. "Charging facility planning and scheduling problems for battery electric bus systems: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
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    10. Lim, Lek Keng & Muis, Zarina Ab & Ho, Wai Shin & Hashim, Haslenda & Bong, Cassendra Phun Chien, 2023. "Review of the energy forecasting and scheduling model for electric buses," Energy, Elsevier, vol. 263(PD).
    11. Jing Wang & Heqi Wang & Ande Chang & Chen Song, 2022. "Collaborative Optimization of Vehicle and Crew Scheduling for a Mixed Fleet with Electric and Conventional Buses," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    12. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    13. Perumal, Shyam S.G. & Lusby, Richard M. & Larsen, Jesper, 2022. "Electric bus planning & scheduling: A review of related problems and methodologies," European Journal of Operational Research, Elsevier, vol. 301(2), pages 395-413.
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