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- W4300166990 abstract "In this study, we address the problem of finding the best locations for mobile labs offering 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 define 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 define 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 defined 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." @default.
- W4300166990 created "2022-10-03" @default.
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- W4300166990 date "2022-10-03" @default.
- W4300166990 modified "2023-09-27" @default.
- W4300166990 title "Accessible location of mobile labs for COVID-19 testing" @default.
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- W4300166990 doi "https://doi.org/10.1007/s10729-022-09614-3" @default.
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