Mobility Data in Operations: The Facility Location Problem

48 Pages Posted: 18 Jan 2023 Last revised: 14 Apr 2023

See all articles by Ozan Candogan

Ozan Candogan

University of Chicago - Booth School of Business

Yiding Feng

Microsoft Corporation - Microsoft Research New England

Date Written: January 16, 2023

Abstract

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 3.103. We complement this positive result by showing that the proposed algorithm is at least a 3.073-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.

Keywords: Facility location problem, vertex cover, mobility data

Suggested Citation

Candogan, Ozan and Feng, Yiding, Mobility Data in Operations: The Facility Location Problem (January 16, 2023). Available at SSRN: https://ssrn.com/abstract=4324967 or http://dx.doi.org/10.2139/ssrn.4324967

Ozan Candogan

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

HOME PAGE: http://faculty.chicagobooth.edu/ozan.candogan/

Yiding Feng (Contact Author)

Microsoft Corporation - Microsoft Research New England ( email )

One Memorial Drive, 14th Floor
Cambridge, MA 02142
United States

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