Network revenue management is concerned with managing demand for products that require
inventory from one or several resources by controlling product availability and/or prices in order
to maximize expected revenues subject to the available resource capacities. One can tackle this
problem by decomposing it into resource-level subproblems that can be solved efficiently, e.g. by
dynamic programming (DP).
We propose a new dynamic fare proration method specifically having large-scale applications in
mind. It decomposes the network problem by fare proration and solves the resource-level dynamic
programs simultaneously using simple, endogenously obtained dynamic marginal capacity value es-
timates to update fare prorations over time. An extensive numerical simulation study demonstrates
that the method results in tightened upper bounds on the optimal expected revenue, and that the
obtained policies are very effective with regard to achieved revenues and required runtime.