  We study the solution of a large-scale transportation problem with an
additional constraint on the sparsity of inbound flows. Such problems arise in
the management of inventory for online retailers that operate with many order
fulfillment centers; each stock-keeping unit is typically kept in a limited
number of these order fulfillment centers so as to reduce the operational
overhead in the supply chain network.
  We propose a computationally efficient algorithm that solves this
sparsity-constrained optimization problem while bypassing complexities of the
conventional integer programming approach. The effectiveness of the algorithm
is demonstrated through a series of numerical experiments on synthetic data.
