Fluid arrivals simulation for choice network revenue management

Abstract

Since the beginning of revenue management, simulation has been used to estimate the expected revenue resulting from an availability policy. It has also been used to verify the quality of forecasts by projecting them onto past availability policies. Recently, it has been used in simulation-based optimization approaches to find the best policy. Simulation thus has a central role in revenue management. We focus on the choice network revenue management (CNRM) problem that incorporates multiple resources and customer behavior. The traditional CNRM simulation is based on discrete customer arrivals; we propose a new approach based on fluid arrivals. Our estimator is biased, but we observe that the bias is often insignificant in practice and appears to be asymptotically null. Our approach consistently outperforms the traditional simulation in terms of estimation time and is thus a better choice for large instances. We also prove that it is equivalent to an approximation for the CNRM availability policy optimization problem. This equivalence limits the value of simulation-based optimization methods but allows us to propose heuristics to rapidly support the optimization.

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Acknowledgements

The authors are grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds de recherche du Quebec en nature et technologies (FRQNT), and ExPretio technologies for funding and supporting this research.

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Correspondence to Thibault Barbier.

Appendix

Appendix

All instances are completely described at https://thibaultbarbier.com.

Table 1 Optimal policies for both instances
Table 2 Bus-line with 150 random policies per PC scenario
Table 3 Airline with 50 random policies per PC scenario

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Barbier, T., Anjos, M., Cirinei, F. et al. Fluid arrivals simulation for choice network revenue management. J Revenue Pricing Manag 18, 164–180 (2019). https://doi.org/10.1057/s41272-018-00172-4

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Keywords

  • Revenue management
  • Fluid arrivals simulation
  • Choice behavior
  • Availability control
  • Simulation-based optimization