  The recent large scale availability of mobility data, which captures
individual mobility patterns, poses novel operational problems that are
exciting and challenging. Motivated by this, we introduce and study a variant
of the (cost-minimization) facility location problem where each individual is
endowed with two locations (hereafter, her home and work locations), and the
connection cost is the minimum distance between any of her locations and its
closest facility. We design a polynomial-time algorithm whose approximation
ratio is at most 2.497. We complement this positive result by showing that the
proposed algorithm is at least a 2.428-approximation, and there exists no
polynomial-time algorithm with approximation ratio $2-\epsilon$ under
UG-hardness. We further extend our results and analysis to the model where each
individual is endowed with K locations. Finally, we conduct numerical
experiments over both synthetic data and US census data (for NYC, greater LA,
greater DC, Research Triangle) and evaluate the performance of our algorithms.
