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Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

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  • Xiaopan Chen
  • Yunfeng Kong
  • Lanxue Dang
  • Yane Hou
  • Xinyue Ye
Abstract
As a class of hard combinatorial optimization problems, the school bus routing problem has received considerable attention in the last decades. For a multi-school system, given the bus trips for each school, the school bus scheduling problem aims at optimizing bus schedules to serve all the trips within the school time windows. In this paper, we propose two approaches for solving the bi-objective school bus scheduling problem: an exact method of mixed integer programming (MIP) and a metaheuristic method which combines simulated annealing with local search. We develop MIP formulations for homogenous and heterogeneous fleet problems respectively and solve the models by MIP solver CPLEX. The bus type-based formulation for heterogeneous fleet problem reduces the model complexity in terms of the number of decision variables and constraints. The metaheuristic method is a two-stage framework for minimizing the number of buses to be used as well as the total travel distance of buses. We evaluate the proposed MIP and the metaheuristic method on two benchmark datasets, showing that on both instances, our metaheuristic method significantly outperforms the respective state-of-the-art methods.

Suggested Citation

  • Xiaopan Chen & Yunfeng Kong & Lanxue Dang & Yane Hou & Xinyue Ye, 2015. "Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-20, July.
    • Handle: RePEc:plo:pone00:0132600
    DOI: 10.1371/journal.pone.0132600
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    References listed on IDEAS

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    Cited by:

    1. Shafahi, Ali & Wang, Zhongxiang & Haghani, Ali, 2018. "SpeedRoute: Fast, efficient solutions for school bus routing problems," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 473-493.
    2. Ansari, Azadeh & Farrokhvar, Leily & Kamali, Behrooz, 2021. "Integrated student to school assignment and school bus routing problem for special needs students," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    3. Dimitris Bertsimas & Arthur Delarue & William Eger & John Hanlon & Sebastien Martin, 2020. "Bus Routing Optimization Helps Boston Public Schools Design Better Policies," Interfaces, INFORMS, vol. 50(1), pages 37-49, January.
    4. Wang, Zhongxiang & Haghani, Ali, 2020. "Column generation-based stochastic school bell time and bus scheduling optimization," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1087-1102.
    5. Banerjee, Dipayan & Smilowitz, Karen, 2019. "Incorporating equity into the school bus scheduling problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 228-246.
    6. Dasdemir, Erdi & Testik, Murat Caner & Öztürk, Diclehan Tezcaner & Şakar, Ceren Tuncer & Güleryüz, Güldal & Testik, Özlem Müge, 2022. "A multi-objective open vehicle routing problem with overbooking: Exact and heuristic solution approaches for an employee transportation problem," Omega, Elsevier, vol. 108(C).
    7. Ellegood, William A. & Solomon, Stanislaus & North, Jeremy & Campbell, James F., 2020. "School bus routing problem: Contemporary trends and research directions," Omega, Elsevier, vol. 95(C).
    8. Phan Nguyen Ky Phuc & Nguyen Le Phuong Thao, 2021. "Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets," Logistics, MDPI, vol. 5(2), pages 1-13, May.

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