Parallel allocation-mission optimization of a 128-route network

Hwang, John T.; Martins, Joaquim R. R. A.

doi:10.2514/6.2015-2321

Cited by 12 publications

(11 citation statements)

References 13 publications

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“…Further, the problem might get computationally very expensive with high-fidelity aircraft design and an increased size of the airline network, as observed earlier by Mane et al [1], thereby establishing the need for a better approach. Follow-up efforts [18,19] extended the three-route allocation-mission problem to consider a more realistic 128-route network and leverage a parallel computing framework.…”

Section: Fig 2 Sequential Decomposition Approach (Adapted From Ref mentioning

confidence: 99%

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

Roy

Crossley

Moore³

et al. 2019

Journal of Aircraft

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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.

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Section: Fig 2 Sequential Decomposition Approach (Adapted From Ref mentioning

confidence: 99%

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

Roy

Crossley

Moore³

et al. 2019

Journal of Aircraft

Self Cite

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“…[1], thereby establishing the need for a better approach. Follow-up efforts [16,17] extended the three-route allocation-mission problem to consider a more realistic 128-route network and leverage a parallel computing framework. The work employs a high-fidelity aerostructural analysis tool for the large airline network problem, has over 6000 design variables, and uses the adjoint-based method to compute the analytic derivatives.…”

Section: Literature Reviewmentioning

confidence: 99%

Next generation aircraft design considering airline operations and economics

Roy

Crossley

Moore

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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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

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“…As Fig. 3 illustrates, this issue is resolved by solving groups of multiple right-hand sides simultaneously [19]. By applying the linear block-Jacobi solver across the mission analyses, each processor would solve the one non-zero right-hand side it has in its group, all simultaneously, rather than waiting for the previous mission analysis to complete its adjoint solution.…”

Section: Large Computational Cost Of the Models-parallelizationmentioning

confidence: 99%

“…Figure 3: The adjoint system from Eq. (1) for a notional two-mission problem where a constraint from the first mission is solved (left), a constraint from the second mission is solved (center), or both constraints are solved simultaneously (right) using a strategy that solves the linear equation with multiple right-hand sides simultaneously [19].…”

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confidence: 99%

Allocation-mission-design optimization of next-generation aircraft using a parallel computational framework

Hwang

Martins

2016

57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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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 aircraft 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 retrained 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.

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Parallel allocation-mission optimization of a 128-route network

Cited by 12 publications

References 13 publications

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

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

Next generation aircraft design considering airline operations and economics

Allocation-mission-design optimization of next-generation aircraft using a parallel computational framework

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