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

Yan-Ting Hou,Feng Chu,Jia-Zhen Huo

doi:10.1155/2019/1812543

Abstract

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.

Highlights

For the development of the world’s airlines in 2017, strong demand for air passenger service drove the expansion of the airline network exceeding 20000 unique city pairs as well as 9% net profit growth in excess of $38.0 billion [1]
In terms of profit indicator, the sample average approximation (SAA) achieves slightly larger values than the genetic algorithm (GA) in 6 among 8 instances. It is reasonable because the value of the best individual within a population can be regarded as the solution of our problem for the GA
In terms of running time indicator, the SAA performs better than the GA in 5 out of 8 instances. These results indicate that the SAA is superior

Summary

Introduction

For the development of the world’s airlines in 2017, strong demand for air passenger service drove the expansion of the airline network exceeding 20000 unique city pairs as well as 9% net profit growth in excess of $38.0 billion [1]. Reference [6] addresses a capacitated single allocation hub location problem and determines the installed capacities for each hub from a finite set of capacity levels with different set-up costs This strategy can largely improve the utilization of the infrastructure and reduce traffic delays. We provide a hybrid two-stage stochastic programming-robust optimization model to maximize a weighted sum of the profit. We present a two-stage stochastic programming to describe the interaction between hub location and revenue management and provide a flexible strategy for the infrastructure by considering a set of capacities available for each hub under disruptions.

Literature Review

Problem Statement

Mathematical Formulations

Solution Approach

Computational Experiments and Discussion

Conclusions