 |
|
|
 |
Joern Meissner, Arne K Strauss
|
|
| Abstract |
|
We develop an approximate dynamic programming approach to network revenue management models with
customer choice that approximates the value function of the Markov decision process with a non-linear
function which is separable across resource inventory levels. This approximation can exhibit significantly
improved accuracy compared to currently available methods. It further allows for arbitrary aggregation
of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal
expected revenue that are provably at least as tight as those obtained from previous approaches. Computational
experiments for the multinomial logit choice model with distinct consideration sets show that policies
derived from our approach can outperform some recently proposed alternatives, and we demonstrate how
aggregation can be used to balance solution quality and runtime. |
|
| Keywords |
|
Revenue Management, Dynamic Programming, Optimal Control, Applications, Approximate
|
|
| Status |
|
European Journal of Operational Research, Vol 216, Issue 2 (January 2012) pp 459–468. |
|
| Download |
|
www.meiss.com/download/RM-Meissner-Strauss.pdf
(331 kb) |
|
| Reference |
|
BibTeX,
Plain Text |
|
|
|
 |
