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Interval-Based Dynamic Discretization Discovery for Solving the Continuous-Time Service Network Design Problem

Author

Listed:
  • Luke Marshall

    (Microsoft Research, Redmond, Washington 98052;)

  • Natashia Boland

    (Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Martin Savelsbergh

    (Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Mike Hewitt

    (Department of Information Systems and Supply Chain Management, Quinlan School of Business, Loyola University Chicago, Chicago, Illinois 60611)

Abstract

We introduce an effective and efficient iterative algorithm for solving the continuous-time service network design problem. The algorithm achieves its efficiency by carefully and dynamically refining partially time-expanded network models so that only a small number of small integer programs, defined over these networks, need to be solved. An extensive computational study shows that the algorithm performs well in practice, often using time-expanded network models with size much less than 1% (in terms of number of variables and constraints) of a full time-expanded network model. The algorithm is inspired by and has many similarities to the dynamic discretization discovery algorithm introduced in Boland et al. [Boland N, Hewitt M, Marshall L, Savelsbergh M (2017) The continuous-time service network design problem. Oper. Res. 65(5):1303–1321.], but generates smaller partially time-expanded models, produces high-quality solutions more quickly, and converges more quickly.

Suggested Citation

  • Luke Marshall & Natashia Boland & Martin Savelsbergh & Mike Hewitt, 2021. "Interval-Based Dynamic Discretization Discovery for Solving the Continuous-Time Service Network Design Problem," Transportation Science, INFORMS, vol. 55(1), pages 29-51, 1-2.
  • Handle: RePEc:inm:ortrsc:v:55:y:2021:i:1:p:29-51
    DOI: 10.1287/trsc.2020.0994
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    References listed on IDEAS

    as
    1. Wang, Xiubin & Regan, Amelia C., 2002. "Local truckload pickup and delivery with hard time window constraints," Transportation Research Part B: Methodological, Elsevier, vol. 36(2), pages 97-112, February.
    2. Natashia Boland & Mike Hewitt & Luke Marshall & Martin Savelsbergh, 2019. "The price of discretizing time: a study in service network design," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(2), pages 195-216, June.
    3. T. L. Magnanti & R. T. Wong, 1984. "Network Design and Transportation Planning: Models and Algorithms," Transportation Science, INFORMS, vol. 18(1), pages 1-55, February.
    4. Natashia Boland & Mike Hewitt & Luke Marshall & Martin Savelsbergh, 2017. "The Continuous-Time Service Network Design Problem," Operations Research, INFORMS, vol. 65(5), pages 1303-1321, October.
    5. Sanjeeb Dash & Oktay Günlük & Andrea Lodi & Andrea Tramontani, 2012. "A Time Bucket Formulation for the Traveling Salesman Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 132-147, February.
    6. Crainic, Teodor Gabriel, 2000. "Service network design in freight transportation," European Journal of Operational Research, Elsevier, vol. 122(2), pages 272-288, April.
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    Cited by:

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    3. Lu, Jiawei & Nie, Qinghui & Mahmoudi, Monirehalsadat & Ou, Jishun & Li, Chongnan & Zhou, Xuesong Simon, 2022. "Rich arc routing problem in city logistics: Models and solution algorithms using a fluid queue-based time-dependent travel time representation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 143-182.

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