Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice
We develop a new approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a concave function which is separable across resource inventory levels. This approach reflects the intuitive interpretation of diminishing marginal utility of inventory levels and allows for significantly improved accuracy compared to currently available methods. The model 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, and is asymptotically optimal under fluid scaling. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach outperform available alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.
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