Next generation aircraft design considering airline operations and economics

Abstract

Traditional approaches to design and optimization of a new system often use a systemcentric objective and do not take into consideration how the operator will use this new system alongside other existing systems. When the new system design is incorporated into the broader group of systems, the performance of the operator-level objective can be sub-optimal due to the unmodeled interaction between the new system and the other systems. Among the few available references that describe attempts to address this disconnect, most follow an MDO-motivated sequential decomposition approach of first designing a very good system and then providing this system to the operator who, decides the best way to use this new system along with the existing systems. This paper addresses this issue by including aircraft design, airline operations, and revenue management "subspaces"; and presents an approach that could simultaneously solve these subspaces posed as a monolithic optimization problem rather than the traditional approach described above. The monolithic approach makes the problem an expensive Mixed Integer Non-Linear Programming problem, which are extremely difficult to solve. To address the problem, we use a recently developed optimization framework that simultaneously solves the subspaces to capture the "synergy" in the problem that the previous decomposition approaches did not exploit, addresses mixed-integer/discrete type design variables in an efficient manner, and accounts for computationally expensive analysis tools. This approach solves an 11-route airline network problem consisting of 94 decision variables including 33 integer and 61 continuous type variables. Simultaneously solving the subspaces leads to significant improvement in the fleet-level objective of the airline when compared to the previously developed sequential subspace decomposition approach. NomenclatureBH a, j = Block hours of aircraft type a on route j dem j = Daily passenger demand on route j E I = Expected Improvement f leet a = Number of aircraft type a k I = Number of integer type design variables of the problem M H a, j = Maintenance hour per block hour of aircraft type a on route j pax a, j = Number of passengers per flight on aircraft type a on route j

Monolithic Approach for Next-Generation Aircraft Design Considering Airline Operations and Economics

“…* The result here shows a higher profit for the simultaneous case compared to Ref. [4,38] due to the improvement in the AMIEGO framework to better explore the integer design space Table 4 also shows the performance data of the "new" aircraft at the design mission range of 2940 nmi for the three scenarios. The aircraft is sized for a 2940 nmi design mission range with 162 seats onboard.…”

“…A detailed description of the AMIEGO algorithm appears in Ref. [4,6,38]. An arbitrary bound assignment may increase the computational cost due to an increased integer design space.…”

A Bilevel Methodology for solving a Structural Optimization Problem with both Continuous and Categorical Variables

“…Finally, it has been observed that in some cases, this current formulation of the Bi-level algorithm retrieves a higher optimal weight than a complete enumeration. This is why another approach, based on the framework AMIEGO [24], is under investigation. The main idea of this framework is to leverage the efficiency of a gradient-based optimizer while globally exploring the integer design space with the help of the Efficient Global Optimization (EGO) [25] algorithm.…”

OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization

“…This work relied heavily on OpenMDAO's support for parallel computing to efficiently evaluate multiple aerodynamic points simultaneously. Related work on this problem by Roy et al (2018a) expanded the problem to a mixed integer optimization that considered the airline allocation portion of the problem in a more correct discrete form, which demonstrated the flexibility of the framework to expand beyond purely gradient-based optimization.…”