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A post-improvement procedure for the mixed load school bus routing problem

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  • Park, Junhyuk
  • Tae, Hyunchul
  • Kim, Byung-In

Abstract

This paper aims to develop a mixed load algorithm for the school bus routing problem (SBRP) and measure its effects on the number of required vehicles. SBRP seeks to find optimal routes for a fleet of vehicles, where each vehicle transports students from their homes and to their schools while satisfying various constraints. When mixed load is allowed, students of different schools can get on the same bus at the same time. Although many of real world SBRP allow mixed load, only a few studies have considered these cases. In this paper, we present a new mixed load improvement algorithm and compare it with the only existing algorithm from the literature. Benchmark problems are proposed to compare the performances of algorithms and to stimulate other researchers’ further study. The proposed algorithm outperforms the existing algorithm on the benchmark problem instances. It has also been successfully applied to some of real-world SBRP and could reduce the required number of vehicles compared with the current practice.

Suggested Citation

  • Park, Junhyuk & Tae, Hyunchul & Kim, Byung-In, 2012. "A post-improvement procedure for the mixed load school bus routing problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 204-213.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:1:p:204-213
    DOI: 10.1016/j.ejor.2011.08.022
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    Cited by:

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    2. 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.
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    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. Amanda Chu & Pinar Keskinocak & Monica C. Villarreal, 2020. "Introduction: Empowering Denver Public Schools to Optimize School Bus Operations," Interfaces, INFORMS, vol. 50(5), pages 298-312, September.
    8. Hernan Caceres & Rajan Batta & Qing He, 2017. "School Bus Routing with Stochastic Demand and Duration Constraints," Transportation Science, INFORMS, vol. 51(4), pages 1349-1364, November.
    9. Ezquerro Eguizábal, Sara & Moura Berodia, José Luis & Ibeas Portilla, Ángel & Benavente Ponce, Juan, 2018. "Optimization model for school transportation design based on economic and social efficiency," Transport Policy, Elsevier, vol. 67(C), pages 93-101.
    10. Fátima M. Souza Lima & Davi S. D. Pereira & Samuel V. Conceição & Ricardo S. Camargo, 2017. "A multi-objective capacitated rural school bus routing problem with heterogeneous fleet and mixed loads," 4OR, Springer, vol. 15(4), pages 359-386, December.
    11. Schittekat, Patrick & Kinable, Joris & Sörensen, Kenneth & Sevaux, Marc & Spieksma, Frits & Springael, Johan, 2013. "A metaheuristic for the school bus routing problem with bus stop selection," European Journal of Operational Research, Elsevier, vol. 229(2), pages 518-528.
    12. 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).
    13. 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).
    14. 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.
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