A smart school routing and scheduling problem for the new normalcy

Jenny Díaz-Ramírez*, Carlos Mario Leal-Garza, Carlos Gómez-Acosta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

One of the critical actions that emerged during the onset of the New Normalcy after COVID-19 lockdowns, is the safe return to schools and workplaces. Therefore, dedicated transportation services need to adapt to meet new requirements such as arrival reliability for multiple bell times, the consequent staggering of arrivals and departures, and the decrease in bus capacity due to the physical distancing required by regulators. In this work, we address these issues plus additional labor conditions concerning drivers for a university context; with the goal of optimizing social interests such as covering demand and travel time under limited resources. We propose a bi-level approach, where firstly a bus routing generation sub-problem is solved before a bus scheduling sub-problem. This (strategic) solution is then considered as the baseline for subsequent dynamic (operational) routing. The latter is based on real-time demand provided by the students via a mobile app and considers stop-skipping to further minimize travel time. This integrated transport solution was tested in a university case, showing that with the same resources, it can meet these new requirements. In addition, numerical experimentation was also carried out with benchmark instances to identify, among available and literature-recommended solution algorithms and an effective tailored Tabu Search implementation, those that perform best for this type of problems.

Original languageEnglish
Article number108101
Pages (from-to)108101
JournalComputers and Industrial Engineering
Volume168
DOIs
Publication statusPublished - Jun 2022

Bibliographical note

Funding Information:
To the Universidad de Monterrey, for data providing and its support to publish this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Publisher Copyright:
© 2022 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering

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  • PEF de Excelencia IIS Otoño 2020

    Gómez-Acosta, Carlos (Recipient), Leal-Garza, Carlos Mario (Recipient) & Díaz Ramírez, Jenny (Recipient), Dec 2020

    Prize: Other distinction

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