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The Monte Carlo first-come-first-served heuristic for network revenue management

Author

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  • Nicolas Houy

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • François Le Grand

    (EM - EMLyon Business School)

Abstract

We introduce the Monte-Carlo based heuristic with first-come-first-served approximation for future optimal strategy (MC-FCFS) in order to maximize profit in a network revenue management problem. Like the randomized linear programming (RLP) model, one purpose of the MC-FCFS heuristic is to have information about displacement costs, considering the full probability distribution of future demands instead of a simplified degenerate distribution as in the deterministic linear programming (DLP) model. However, this information is conveyed by applying the FCFS heuristic as a future strategy rather than using the optimal ex-post profits as in the RLP heuristic. We show that MC-FCFS performs approximately as well as the RLP heuristic at a much lower computational cost and much better than the DLP heuristic at maximizing profit in a multi-night hotel booking setting with or without planned upgrades.

Suggested Citation

  • Nicolas Houy & François Le Grand, 2015. "The Monte Carlo first-come-first-served heuristic for network revenue management," Working Papers halshs-01155698, HAL.
    • Handle: RePEc:hal:wpaper:halshs-01155698
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01155698
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    References listed on IDEAS

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    Keywords

    Network revenue management; Monte-Carlo simulations; randomized linear programming;
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