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A smart school routing and scheduling problem for the new normalcy.

Jenny Díaz-Ramírez1, Carlos Mario Leal-Garza1, Carlos Gómez-Acosta1

  • 1Universidad de Monterrey, Av. Ignacio Morones Prieto 4500-Pte., 66238 San Pedro Garza García, N.L., Mexico.

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Summary

This study presents an optimized transportation system for universities adapting to new safety requirements. The integrated solution ensures reliable student transport with reduced capacity and driver considerations.

Keywords:
Bus scheduling problemCOVID-19Multiple bell timesNew normalcyOn-time reliabilitySchool bus routing problemStop-skipping

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Area of Science:

  • Operations Research
  • Transportation Logistics
  • Public Health Adaptations

Background:

  • The COVID-19 pandemic necessitated adaptations in public transportation for the safe return to schools and workplaces.
  • New requirements include arrival reliability for staggered schedules, reduced bus capacity due to physical distancing, and driver welfare.
  • University transportation services face unique challenges in balancing demand, travel time, and resource limitations under these new conditions.

Purpose of the Study:

  • To develop and evaluate an optimized, integrated transportation solution for universities operating under post-COVID-19 safety regulations.
  • To address challenges of arrival reliability, staggered scheduling, reduced capacity, and driver conditions within a university transport context.
  • To optimize social interests by balancing service demand coverage and minimizing travel time with limited resources.

Main Methods:

  • A bi-level optimization approach was proposed, integrating strategic bus routing and scheduling with dynamic operational routing.
  • The strategic phase involved solving bus routing and scheduling sub-problems.
  • The operational phase utilized real-time student demand via a mobile app and incorporated stop-skipping strategies for travel time minimization. Performance of solution algorithms, including a tailored Tabu Search, was evaluated using benchmark instances.

Main Results:

  • The integrated transport solution demonstrated the ability to meet new safety and operational requirements within a university setting using existing resources.
  • Numerical experimentation identified effective solution algorithms for this class of transportation optimization problems.
  • The proposed system successfully balanced demand coverage and travel time optimization under resource constraints.

Conclusions:

  • The developed bi-level optimization approach provides an effective framework for adapting university transportation systems to the 'New Normalcy'.
  • The integration of strategic planning with real-time operational adjustments, including mobile app data and stop-skipping, enhances transport efficiency and reliability.
  • The study confirms the feasibility of meeting new public health mandates while optimizing university transportation logistics and driver conditions.