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Models and Algorithms for Single-Depot Vehicle Scheduling

Author

Listed:
  • Richard Freling

    (Econometric Institute, Erasmus University, Rotterdam, The Netherlands)

  • Albert P. M. Wagelmans

    (DEIO, Universidade de Lisboa, Lisbon, Portugal)

  • José M. Pinto Paixão

    (DEIO, Universidade de Lisboa, Lisbon, Portugal)

Abstract

Vehicle scheduling is the process of assigning vehicles to a set of predetermined trips with fixed starting and ending times, while minimizing capital and operating costs. This paper considers modeling, algorithmic, and computational aspects of the polynomially solvable case in which there is a single depot and vehicles are identical. A quasiassignment formulation is reviewed and an alternative asymmetric assignment formulation is given. The main contributions of the paper are a new two-phase approach which is valid in the case of a special cost structure, an auction algorithm for the quasiassignment problem, a core-oriented approach, and an extensive computational study. New algorithms are compared with the most successful algorithms for the vehicle-scheduling problem, using both randomly generated and real-life data. The new algorithms show a significant performance improvement with respect to computation time. Such improvement can, for example, be very important when this particular vehicle-scheduling problem appears as a subproblem in more complex vehicle- and crew-scheduling problems.

Suggested Citation

  • Richard Freling & Albert P. M. Wagelmans & José M. Pinto Paixão, 2001. "Models and Algorithms for Single-Depot Vehicle Scheduling," Transportation Science, INFORMS, vol. 35(2), pages 165-180, May.
    • Handle: RePEc:inm:ortrsc:v:35:y:2001:i:2:p:165-180
    DOI: 10.1287/trsc.35.2.165.10135
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    References listed on IDEAS

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    6. Freling, R. & Pinto Paixão, J.M. & Wagelmans, A.P.M., 1995. "Models and Algorithms for Vehicle Scheduling," Econometric Institute Research Papers EI 9562-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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    Cited by:

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    2. Weiszer, Michal & Chen, Jun & Locatelli, Giorgio, 2015. "An integrated optimisation approach to airport ground operations to foster sustainability in the aviation sector," Applied Energy, Elsevier, vol. 157(C), pages 567-582.
    3. Richard Eglese & Sofoclis Zambirinis, 2018. "Disruption management in vehicle routing and scheduling for road freight transport: a review," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 1-17, April.
    4. J Faulin & A García del Valle, 2008. "Solving the capacitated vehicle routing problem using the ALGELECT electrostatic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(12), pages 1685-1695, December.
    5. Satish Sharma & Somesh Bhattacharya & Deep Kiran & Bin Hu & Matthias Prandtstetter & Brian Azzopardi, 2023. "Optimizing the Scheduling of Electrified Public Transport System in Malta," Energies, MDPI, vol. 16(13), pages 1-16, June.
    6. 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.
    7. Uçar, Ezgi & İlker Birbil, Ş. & Muter, İbrahim, 2017. "Managing disruptions in the multi-depot vehicle scheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 249-269.
    8. Bixuan Sun & Jeffrey Apland, 2019. "Operational planning of public transit with economic and environmental goals: application to the Minneapolis–St. Paul bus system," Public Transport, Springer, vol. 11(2), pages 237-267, August.
    9. Tomoshi Otsuki & Kazuyuki Aihara, 2016. "New variable depth local search for multiple depot vehicle scheduling problems," Journal of Heuristics, Springer, vol. 22(4), pages 567-585, August.
    10. Dennis Huisman & Richard Freling & Albert P. M. Wagelmans, 2004. "A Robust Solution Approach to the Dynamic Vehicle Scheduling Problem," Transportation Science, INFORMS, vol. 38(4), pages 447-458, November.

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