  One innovative solution to the last-mile delivery problem is the self-service
locker system. Motivated by a real case in Singapore, we consider a POP-Locker
Alliance who operates a set of POP-stations and wishes to improve the last-mile
delivery by opening new locker facilities. We propose a quantitative approach
to determine the optimal locker location with the objective to maximize the
overall service provided by the alliance. Customer's choices regarding the use
of facilities are explicitly considered. They are predicted by a multinomial
logit model. We then formulate the location problem as a multi-ratio
linear-fractional 0-1 program and provide two solution approaches. The first
one is to reformulate the original problem as a mixed-integer linear program,
which is further strengthened using conditional McCormick inequalities. This
approach is an exact method, developed for small-scale problems. For
large-scale problems, we propose a Suggest-and-Improve framework with two
embedded algorithms. Numerical studies indicated that our framework is an
efficient approach that yields high-quality solutions. Finally, we conducted a
case study. The results highlighted the importance of considering the
customers' choices. Under different parameter values of the multinomial logit
model, the decisions could be completely different. Therefore, the parameter
value should be carefully estimated in advance.
