  We introduce a strategic decision-making problem faced by logistics providers
(LPs) seeking facility location decisions that lead to profitable operations.
The profitability depends on the revenue generated through agreements with
shippers, and the costs arising when satisfying those agreements. The latter
depend in turn on service levels and on characteristics of the shippers'
customers. However, at a strategic level, LP has imperfect information thereof.
  We propose a stochastic bilevel formulation where a given LP (leader)
anticipates the decisions of shippers (followers) arising from a random utility
maximization model. Using a sample average approximation and properties of the
associated optimal solutions, we introduce a non-conventional single-level
mixed integer linear programming formulation that can be solved by a
general-purpose solver. We can quickly identify situations that lead to zero
expected profit for the LP. Experimental results show that optimal expected
profit is highly dependent on shippers' price sensitivity. Underestimating it
can lead to an overestimation of expected profits.
