Typically, computational design optimization of commercial aircraft is performed considering a small number of representative operating conditions. These conditions are based on the types of missions and the Mach number, altitude, and other operational profiles for which the aircraft will be flown. However, the design also influences which routes and mission parameters are optimal, so there is coupling that is ignored when using this approach. Here, we aim to simultaneously optimize the

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... design, mission profiles, and the allocation of aircraft to routes in an airline network. To enable this, we use a gradient-based optimization approach with a parallel computational framework that facilitates the computation of derivatives and the multidisciplinary analysis. We use a surrogate model for the CFD analysis that is re-trained in each optimization iteration given the new set of shape design variables. The resulting optimization problem contains over 6,000 design variables and 23,000 constraints, and we solve it in roughly 10 hours on 128 processors. The optimization results show a 27% increase in airline profit when comparing the allocation-mission-design optimization to allocationonly optimization. The resulting aircraft shape design differs from a conventional multipoint design optimization, which confirms the need for the new approach. Its significance is that it produces aircraft designs that are optimal with respect to the airline-level profit, and the algorithm can quantify in these terms the potential benefits of next-generation aircraft.