Expected Future Value Decomposition Based Bid Price Generation For Large-Scale Network Revenue Management



This paper contributes to the understanding of how firms best set prices. Specifically it studies a multi-stage stochastic programming model for large-scale network revenue management. We solve the model by means of the so-called Expected Future Value (EFV) decomposition via scenario analysis, estimating the impact of the decisions made at a given stage on the objective function value related to the future stages. The EFV curves are used to define bid prices on bundles of resources directly, as opposed to the traditional additive bid prices. Numerical results show that the revenue outcome of our approach is generally comparable to that of state-of-the-art additive approaches, and tends to be better when the network structure is complex. Moreover, our approach requires significantly less computation time than the optimization of the compact representation by plain use of optimization engines.

Dr Dolores Romero Morales is a University Reader (Associate Professor) at Saïd Business School and a Fellow of St. Cross College, University of Oxford. Her area of specialization is Operations Research. She holds a PhD from Rotterdam School of Management, Erasmus University Rotterdam, and an MSc in Mathematics from the University of Seville. She has previously held posts at the Universities of Seville, Cádiz, and Maastricht. The core topics of her research are Supply Chain Optimization, Data Mining and Revenue Management. She has published over 20 articles in outlets such as Management Science, Operations Research, INFORMS Journal on Computing and European Journal of Operational Research. At SBS she teaches the core course on Decision Science in the MBA and EMBA programmes. She is the director of the SBS DPhil Programme.

Contact information:
Dr. Peter van Baalen