ABSTRACT In this article we reframe the massive location choice problem for retail chains by proposing an optimization model that integrates human mobility. Traditional methods of massive location choice encounter limitations rooted in assumptions such as power‐law distance decay and oversimplified travel patterns. In response, we present a spatial operations research model aimed at maximizing customer coverage, using massive individual trajectories as a robust “sampling” of human flows. Using a deduplication‐based greedy algorithm, we maximize customer coverage within a predefined number of stores while maintaining computational efficiency. Through a case study in Shenzhen, China, we demonstrate that our model significantly improves population coverage compared to existing retail locations. Additionally, the optimized coverage follows a power‐law distribution, providing implications for the scaling effects and robustness of retail location potential.
Jiang et al. (Thu,) studied this question.