We present an approximation algorithm for network revenue management problems. In our approximation algorithm, we construct an approximate policy using value function approximations that are expressed as linear combinations of basis functions. We use a backward recursion to compute the coefficients of the basis functions in the linear combinations. If each product uses at most L resources, then the total expected revenue obtained by our approximate policy is at least 1/(1+L) of the optimal total expected revenue. In many network revenue management settings, although the number of resources and products can become large, the number of resources used by a product remains bounded. In this case, our approximate policy provides a constant-factor performance guarantee. Our approach can incorporate the customer choice behavior among the products and allows the products to use multiple units of a resource, while still maintaining the performance guarantee.
This is joint work with Yuhang Ma (Cornell), Paat Rusmevichientong (USC) and Mika Sumida (Cornell).
Bio:
Huseyin Topaloglu is a professor in the School of Operations Research and Information Engineering at Cornell University. His research interests include stochastic programming and approximate dynamic programming with applications in transportation logistics, revenue management and supply chain management. His recent work focuses on constructing tractable solution methods for large-scale network revenue management problems and building approximation strategies for retail assortment planning.
