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Pré-publication, Document de travail Année : 2015

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

Résumé

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.
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Dates et versions

halshs-01155698, version 1 (27-05-2015)

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  • HAL Id : halshs-01155698 , version 1

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Nicolas Houy, François Le Grand. The Monte Carlo first-come-first-served heuristic for network revenue management. 2015. ⟨halshs-01155698⟩
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Dernière date de mise à jour le 28/04/2024
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