Monolithic Approach for 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 that does not consider 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 an optimal 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. The monolithic approach makes the problem an expensive MINLP problem and is extremely difficult to solve. We use a recently developed optimization framework that simultaneously solves the subspaces to capture the "synergy" in the problem. The results demonstrate that simultaneously optimizing the subspaces leads to significant improvement in the fleet-level objective of the airline when compared to the previously developed sequential subspace decomposition approach. The results also showcase that maximizing revenue and minimizing operating cost independently need not lead to a maximized profit solution for the airline.

A Hybrid Approach for Solving Constrained Multi-Objective Mixed-Discrete Nonlinear Programming Engineering Problems

“…This allows the gradient-based approach to efficiently solve problems with a very large number of design variables. However, if the objectives are encapsulated in a black-box function and are computationally very expensive to evaluate, then it may not be possible to directly implement a gradient-based search and may require a surrogate-based design optimization approach [40,42,43].…”