| Sumario: | In this study, we address the problem of fnding the best locations for mobile labs ofering COVID-19 testing. We assume that people within known demand centroids have a degree of mobility, i.e., they can travel a reasonable distance, and mobile labs have a limited-and-variable service area. Thus, we defne a location problem concerned with optimizing a measure representing the accessibility of service to its potential clients. In particular, we use the concepts of classical, gradual, and cooperative coverage to defne a weighted sum of multiple accessibility indicators. We formulate our optimization problem via a mixed-integer linear program which is intractable by commercial solvers for large instances. In response, we designed a Biased Random-Key Genetic Algorithm to solve the defned problem; this is capable of obtaining high-quality feasible solutions over large numbers of instances in seconds. Moreover, we present insights derived from a case study into the locations of COVID-19 testing mobile laboratories in Nuevo Leon, Mexico. Our experimental results show that our optimization approach can be used as a diagnostic tool to determine the number of mobile labs needed to satisfy a set of demand centroids, assuming that users have reduced mobility due to the restrictions because of the pandemic.
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