Article ID: | iaor2016767 |
Volume: | 67 |
Issue: | 3 |
Start Page Number: | 402 |
End Page Number: | 411 |
Publication Date: | Mar 2016 |
Journal: | Journal of the Operational Research Society |
Authors: | Meissner Joern, Koenig Matthias |
Keywords: | management, decision, programming: dynamic, combinatorial optimization, programming: markov decision, production |
Consider a risk‐averse decision maker in the setting of a single‐leg dynamic revenue management problem with revenue controlled by limiting capacity for a fixed set of prices. Instead of focussing on maximising the expected revenue, the decision maker has the main objective of minimising the risk of failing to achieve a given target revenue. Interpreting the revenue management problem in the framework of finite Markov decision processes, we augment the state space of the risk‐neutral problem definition and change the objective function to the probability of failing a certain specified target revenue. This enables us to obtain a dynamic programming solution that generates the policy minimising the risk of not attaining this target revenue. We compare this solution with recently proposed risk‐sensitive policies in a numerical study and discuss advantages and limitations.