Airline revenue management games with simultaneous price and quantity competition

Grauberger, Waldemar; Kimms, Alf

doi:10.1016/j.cor.2016.05.008

Cited by 22 publications

(11 citation statements)

References 17 publications

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“…ey considered that a support hub could be applied to supply demand when a hub goes out of order. Grauberger and Kimms [20] investigated an airline revenue management problem considering price competition and limited capacity. He [21] studied the revenue management effect on a hub-tohub network.…”

Section: Literature Reviewmentioning

confidence: 99%

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

2022

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The aviation industry is one of the most widely used applications in transportation. Due to the limited capacity of aircraft, revenue management in this industry is of high significance. On the other hand, the hub location problem has been considered to facilitate the demands assignment to hubs. This paper presents an integrated p-hub location and revenue management problem under uncertain demand to maximize net revenue and minimize total cost, including hub establishment and transportation costs. A fuzzy programming model and a genetic algorithm are developed to solve the proposed model with different sizes. The mining and petroleum industry is used for case studies. Results show that the proposed algorithm can obtain a suitable solution in a reasonable amount of time.

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Section: Literature Reviewmentioning

confidence: 99%

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

2022

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“…[16] studies the capacity allocation in a two-airline alliance under competitions in a hub-and-spoke network. [17] studies the airline revenue management with consideration of a competitor's behavior under simultaneous price and capacity competition in a hub-and-spoke network. [18] explores the impact of structural properties on a hubto-hub network revenue management problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem

Huo

Hou

2019

Discrete Dynamics in Nature and Society

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This paper investigates joint decisions on airline network design and capacity allocation by integrating an uncapacitated single allocation p-hub median location problem into a revenue management problem. For the situation in which uncertain demand can be captured by a finite set of scenarios, we extend this integrated problem with average profit maximization to a combined average-case and worst-case analysis of this integration. We formulate this problem as a two-stage stochastic programming framework to maximize the profit, including the cost of installing the hubs and a weighted sum of average and worst case transportation cost and the revenue from tickets over all scenarios. This model can give flexible decisions by putting the emphasis on the importance of average and worst case profits. To solve this problem, a genetic algorithm is applied. Computational results demonstrate the outperformance of the proposed formulation.

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“…This stream can be classified by the network topology: on the one hand, for multiple legs without a specific network structure, some research ( [24,25]) decomposes multiple legs into many single-leg problems and then solves each single-leg revenue management problem. The research on the hub-and-spoke network also has received significant attention ( [26][27][28]) on the other hand. Reference [26] considers a network revenue management problem in a hub-and-spoke network consisting of a hub and spokes.…”

Section: An Integrated Hub Location and Revenue Managementmentioning

confidence: 99%

“…Reference [27] studies the capacity allocation in a two-airline alliance under competitions in a combined hub-and-spoke network. Reference [28] addresses an airline revenue management problem with consideration of a competitor's behavior under simultaneous price and capacity competitions in a hub-and-spoke network. Reference [29] explores the impact of the structural properties in a hub-to-hub network revenue management problem.…”

Section: An Integrated Hub Location and Revenue Managementmentioning

confidence: 99%

An Integrated Problem of p-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions

Hou

Huo

2019

Journal of Advanced Transportation

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This paper considers an integrated hub location and revenue management problem in which a set of capacities is available from which one can be chosen for each hub and the disruption is considered in a star-star shaped airline network. We propose a two-stage stochastic programming model to maximize the profit of the network in which the cost of installing the hubs at different levels of capacities, the transportation cost, and the revenue obtained by selling airline tickets are considered. To provide flexible solutions, a hybrid two-stage stochastic programming-robust optimization model is developed by putting relative emphasis on a weighted sum of profit maximization. Furthermore, a sample average approximation approach is used for solving the stochastic programming formulation and a genetic algorithm approach is applied for both formulations. Numerical experiments are conducted to verify the mathematical formulations and compare the performance of the used approaches.

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Airline revenue management games with simultaneous price and quantity competition

Cited by 22 publications

References 17 publications

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem

An Integrated Problem of p-Hub Location and Revenue Management with Multiple Capacity Levels under Disruptions

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