Stochastic Airline Fleet Assignment With Risk Aversion

Abstract

The air transport industry is an important branch of the intelligent transportation system (ITS). It is widely admitted that modern ITS technologies and advanced management methods, such as fleet assignment, aircraft maintenance routing, and crew scheduling, can significantly increase an airline’s market share and profit, and also improve customer satisfaction. This paper studies a new airline stochastic fleet assignment problem with random passenger demands under risk aversion. The objective is to maximize the expected total profit at a certain level of risk avoidance (i.e., conditional value-at-risk). To solve this problem, we present a risk-averse two-stage stochastic mixed-integer programming model. The first stage mainly deals with tactic level decisions: assigning aircraft families (e.g., Airbus A380 family) to flight legs. The operational level decisions are made in the second stage to efficiently assign aircraft types (e.g., Airbus A380-800 or A380-800F) to flight legs while meeting the family assignment plan developed in the first stage. Then, a sample average approximation algorithm is proposed to solve the stochastic programming problem considering risk aversion. A realistic international airline’s numerical experiment is conducted to illustrate the efficiency of the proposed two-stage stochastic programming model and algorithm.