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Routing a Vehicle to Collect Data After an Earthquake In the immediate aftermath of a major earthquake, it is crucial to quickly and accurately assess structural damage throughout the region. It is especially important to identify buildings that have become unsafe in order to prioritize evacuation efforts. Only a very small number of building inspections can be feasibly performed in a narrow time frame; however, their results can then be combined with other data sources to predict damage at other locations that were not inspected. In “D-Optimal Orienteering for Postearthquake Reconnaissance Planning,” Wang, Xie, Ryzhov, Marković, and Ou present a novel nonlinear integer program that combines vehicle routing with a statistical objective, the goal being to maximize data quality. An exact method based on row and column generation is developed to solve problems with up to 200 buildings. The approach is validated in a realistic case study using real-world building data obtained from a state-of-the-art earthquake simulator. 

This paper seeks to improve an underutilized conventional bus route by converting it into a semiflexible transit system where passengers provide advance notice of their intended stops, allowing buses to skip downstream stops without demand by taking shortcuts. This approach increases stop density, reduces walking distances to and from bus stops, and maintains operational efficiency. To design this system, we develop optimization models that maximize the number of stops while adhering to tour duration and arrival time constraints. A case study in Allegany County, Maryland, demonstrates significant enhancements for routes that were both underutilized (where the probability of a stop lacking demand exceeded 45%) and had layouts conducive to substantial shortcuts. In these instances, the number of stops can be increased by up to 160%, with the actual improvement depending on route configuration, passenger demand, and advance notice requirements. Funding: Financial support from the the National Science Foundation [Grant 2055347] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0561 .