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Distributionally Robust Fair Transit Resource Allocation During a Pandemic

Author

Listed:
  • Luying Sun

    (Department of Industrial & Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061)

  • Weijun Xie

    (Department of Industrial & Systems Engineering, Virginia Tech, Blacksburg, Virginia 24061)

  • Tim Witten

    (Blacksburg Transit, Blacksburg, Virginia 24060)

Abstract

This paper studies the distributionally robust fair transit resource allocation model (DrFRAM) under the Wasserstein ambiguity set to optimize the public transit resource allocation during a pandemic. We show that the proposed DrFRAM is highly nonconvex and nonlinear, and it is NP-hard in general. Fortunately, we show that DrFRAM can be reformulated as a mixed integer linear programming (MILP) by leveraging the equivalent representation of distributionally robust optimization and monotonicity properties, binarizing integer variables, and linearizing nonconvex terms. To improve the proposed MILP formulation, we derive stronger ones and develop valid inequalities by exploiting the model structures. Additionally, we develop scenario decomposition methods using different MILP formulations to solve the scenario subproblems and introduce a simple yet effective no one left-based approximation algorithm with a provable approximation guarantee to solve the model to near optimality. Finally, we numerically demonstrate the effectiveness of the proposed approaches and apply them to real-world data provided by the Blacksburg Transit.

Suggested Citation

  • Luying Sun & Weijun Xie & Tim Witten, 2023. "Distributionally Robust Fair Transit Resource Allocation During a Pandemic," Transportation Science, INFORMS, vol. 57(4), pages 954-978, July.
    • Handle: RePEc:inm:ortrsc:v:57:y:2023:i:4:p:954-978
    DOI: 10.1287/trsc.2022.1159
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