Commercial airlines need to balance their environmental footprint with operational cost when managing the assignment of aviation fleets. These assignments are typically made under uncertainty in terms of their cost (change in labor cost, fuel prices, etc.) and impact on the environment (weather, rules and regulations, etc). Trade-offs of this kind are complicated, and often result in sub-optimal decisions. In this paper, by considering random demand, fare price and avgas price, we propose a multi-criteria method to solve fleet assignment problems. It involves the use of an objective, such as minimizing emission cost or maximizing profit, as a target or a level of aspiration, and the construction of a solution that deviates least from the given target in the L2-norm. We discuss the theoretical advantage of this approach and assess its performance using benchmark data on two test cases-Jetstar Asia, and a major Chinese airline. The Jetstar Asia case is first used to test the effectiveness of this approach in balancing profit against emission by employing an integrated form of simulated and open-source acquisition data. For the case of the Chinese airline, we develop a simple rounding algorithm to deal with the large-scale problem. All cases exhibited the superiority of our compromise approach over the linear-weighted-sum method in terms of accessing the utopia point in discrete objective space. An out-of-sample test revealed the vicinity of the compromise solutions to the Pareto solutions. Our method also yielded better assignment than the airline’s existing strategy in terms of both profit and emissions reduction.
Collected and summarized from the source below by Quynh Hoa https://db.vista.gov.vn:2095/science/article/pii/S0969699716303568