A Mixed Integer Efficient Global Optimization Algorithm for the Simultaneous Aircraft Allocation-Mission-Design Problem

Roy, Satadru; Moore, Kenneth T.; Hwang, John T.; Gray, Justin S.; Crossley, William A.; Martins, Joaquim R. R. A.

doi:10.2514/6.2017-1305

Cited by 19 publications

(25 citation statements)

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“…In this paper we present an approach to solve the challenge problem: design-allocation-revenue management (DARM) posed as a single monolithic optimization problem that captures the interactions between the subspaces (also appears in Ref. [4]). Combining these subspaces makes the problem a Mixed Integer Non-Linear Programming (MINLP) problem and is very hard to solve, if not impossible with available solvers.…”

Section: Fig 1 Overview Of the Design Allocation And The Revenue Mamentioning

confidence: 99%

“…To address this challenge problem, we use the recently developed optimization framework AMIEGO [6] available as a driver within NASA's OpenMDAO [7]. AMIEGO employs a hybrid approach combining features from several different optimization algorithms.…”

Section: Fig 1 Overview Of the Design Allocation And The Revenue Mamentioning

confidence: 99%

“…As mentioned earlier, the combined design-allocation-revenue management is an MINLP problem and is very difficult to solve given the presence of both integer and continuous type design variables and expensive and sophisticated analysis tools. The work here uses the recently developed EGO-like optimization algorithm AMIEGO, named as A Mixed Integer Efficient Global Optimization [6], to solve the aircraft design-allocation-revenue management problem in a simultaneous approach. Preliminary results demonstrate [4,6] that AMIEGO has been successful in addressing several test problems of varying difficulty levels.…”

Section: A Solving the Problemmentioning

confidence: 99%

“…The work here uses the recently developed EGO-like optimization algorithm AMIEGO, named as A Mixed Integer Efficient Global Optimization [6], to solve the aircraft design-allocation-revenue management problem in a simultaneous approach. Preliminary results demonstrate [4,6] that AMIEGO has been successful in addressing several test problems of varying difficulty levels. An overview of the AMIEGO algorithm appears in Fig.3 and a brief description of each step as outlined in Ref.…”

Section: A Solving the Problemmentioning

confidence: 99%

“…An overview of the AMIEGO algorithm appears in Fig.3 and a brief description of each step as outlined in Ref. [4,6] follows below. The red blocks use the EGO framework that explores the integer design space, while the blue block leverages the use of a gradient-based approach to explore the large scale continuous design space.…”

Section: A Solving the Problemmentioning

confidence: 99%

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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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Section: Fig 1 Overview Of the Design Allocation And The Revenue Mamentioning

confidence: 99%

Section: Fig 1 Overview Of the Design Allocation And The Revenue Mamentioning

confidence: 99%