Revenue Management in a Dynamic Network Environment

We investigate dynamic policies for allocating scarce inventory to stochastic demand for multiple fare classes, in a network environment so as to maximize total expected revenues. Typical applications include sequential reservations for an airline network, hotel, or car rental service. We propose an...

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Bibliographic Details
Published in:Transportation science 2003-08, Vol.37 (3), p.257-277
Main Authors: Bertsimas, Dimitris, Popescu, Ioana
Format: Article
Language:English
Subjects:
Air fares
Airlines
Algorithms
Analytical forecasting
Applied sciences
Bid prices
Data analysis
Decision making
Dynamic programming
Exact sciences and technology
Fines & penalties
Ground, air and sea transportation, marine construction
Hotels & motels
Income
Inventory
Inventory control
Inventory management
Linear programming
Management
Networks
Opportunity costs
Optimal solutions
Optimization techniques
Overbooking
Random variables
Rentals
Revenue
Revenue management
Sales
Shadow prices
Transport
Transportation
Transportation planning, management and economics
Wage & price controls
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Summary:We investigate dynamic policies for allocating scarce inventory to stochastic demand for multiple fare classes, in a network environment so as to maximize total expected revenues. Typical applications include sequential reservations for an airline network, hotel, or car rental service. We propose and analyze a new algorithm based on approximate dynamic programming, both theoretically and computationally. This algorithm uses adaptive, nonadditive bid prices from a linear programming relaxation. We provide computational results that give insight into the performance of the new algorithm and the widely used bid-price control, for several networks and demand scenarios. We extend the proposed algorithm to handle cancellations and no-shows by incorporating oversales decisions in the underlying linear programming formulation. We report encouraging computational results that show that the new algorithm leads to higher revenues and more robust performance than bid-price control.
ISSN:0041-1655
1526-5447
DOI:10.1287/trsc.37.3.257.16047