Submodularity and local search approaches for maximum capture problems under generalized extreme value models

We study the maximum capture problem in facility location under random utility models, i.e., the prob-lem of seeking to locate new facilities in a competitive market such that the captured user demand is maximized, assuming that each customer chooses among all available facilities according to a random utility maximization model. We employ the generalized extreme value (GEV) family of discrete choice models and show that the objective function in this context is monotonic and submodular. This find-ing implies that a simple greedy heuristic can always guarantee a (1 - 1/e ) approximation solution. We further develop a new algorithm combining a greedy heuristic, a gradient-based local search, and an ex-changing procedure to efficiently solve the problem. We conduct experiments using instances of different sizes and under different discrete choice models, and we show that our approach significantly outper-forms prior approaches in terms of both returned objective value and CPU time. Our algorithm and the-oretical findings can be applied to the maximum capture problems under various random utility models in the literature, including the popular multinomial logit, nested logit, cross nested logit, and mixed logit models.(C) 2021 Elsevier B.V. All rights reserved.