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.
Bibliographical noteFunding 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.
© 2022 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Computer Science(all)