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

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.

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

“…In another category, it is derived from a combination of pricing and hub location [10]. Finally, integrated revenue management and hub location are considered another category [11,12]. Hörhammer [13] studied a dynamic multiperiod hub location problem with multiple capacity levels.…”

“…A MIP model is developed to minimize transportation and hub installation costs. Hou et al [12] presented an integrated p-hub location and revenue management problem considering multiple capacities under disruptions. A two-stage stochastic model is developed to maximize net profit in which hub installation cost, shipment cost, and revenue obtained from ticket selling are considered.…”

“…Equation (11) states that there are precisely p-hubs in the network. Equation (12) enforces that a nonhub node has been allocated to a hub node if this node had been selected as a hub. Equations ( 13) and (14) show that the maximum number of sold tickets equals demand and the protection level, respectively.…”

Solving Hub Location Problems With Profits Using Variable Neighborhood Search

“…Sharma et al ( 9 ) proposed a two-phase mixed-integer linear programming model for the “coopetitive” profit maximization HLP in the airline industry. Hou et al ( 42 , 43 ) integrated the revenue management and hub location into a revenue management problem to maximize the profits. Taherkhani et al ( 44 ) studied profit maximization capacitated multiple allocation HLPs with the complete hub network under two different types of uncertainty: stochastic demands and uncertain revenue.…”

Effectiveness of dynamic pricing strategy in presence of supply chain disruptions

“…Hosseini-Motlagh et al [16] derived the optimal pricing, corporate social responsibility and sustainability decisions by considering demand disruptions for centralized and decentralized reverse supply chain systems. Hou et al [17] addressed a joint problem consisting of hub location and revenue management problem in airline networks by considering disruptions. Rahmani, Yavari, [18] analyzed pricing and greening decisions in a dual-channel green supply chain with demand disruptions for centralized and decentralized cases.…”