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Research Papers: Design Automation

Bio-Inspired Heuristic Network Configuration in Air Transportation System-of-Systems Design Optimization

[+] Author and Article Information
Ibrahim M. Chamseddine

Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0G4, Canada
e-mail: ibrahim.chamseddine@mail.mcgill.ca

Michael Kokkolaras

Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0G4, Canada
e-mail: michael.kokkolaras@mcgill.ca

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 4, 2016; final manuscript received April 27, 2017; published online June 14, 2017. Assoc. Editor: Christopher Mattson.

J. Mech. Des 139(8), 081401 (Jun 14, 2017) (8 pages) Paper No: MD-16-1620; doi: 10.1115/1.4036778 History: Received September 04, 2016; Revised April 27, 2017

Previous work in air transportation system-of-systems (ATSoSs) design optimization considered integrated aircraft sizing, fleet allocation, and route network configuration. The associated nested multidisciplinary formulation posed a numerically challenging blackbox optimization problem; therefore, direct search methods with convergence properties were used to solve it. However, the complexity of the blackbox impedes greatly the solution of larger-scale problems, where the number of considered nodes in the route network is high. The research presented here adopts a rule-based route network design inspired by biological transfer principles. This bio-inspired approach decouples the network configuration problem from the optimization loop, leading to significant numerical simplifications. The usefulness of the bio-inspired approach is demonstrated by comparing its results to those obtained using the nested formulation for a 15 city network. We then consider introduction of new aircraft as well as a larger problem with 20 cities.

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Figures

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Fig. 1

Nested ATSoS optimization formulation

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Fig. 2

ATSoS optimization formulation with bio-inspired network configuration

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Fig. 3

Schematic of the fractal structure of a bronchial tree

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Fig. 4

Airports connected based on bio-inspired rules

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Fig. 5

Bio-inspired route networks of the first three hubs for the 15-city problem. (a) The main hub is Toronto. Kelowna and Victoria are connected to Toronto through Edmonton, Saskatoon and Regina through Thunder Bay, and St. John’s through Halifax. (b) The main hub is Vancouver. Kelowna is connected to Vancouver through Saskatoon, Thunder Bay and Quebec City through Montreal, and Halifax and St. John’s through Toronto. (c) The main hub is Calgary. Thunder Bay and Quebec City are connected to Calgary through Montreal, and Halifax and St. John’s through Toronto.

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Fig. 6

Cost savings incurred by updating the fleet with a new aircraft as a function of capacity. The star denotes cost savings before considering a new aircraft.

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Fig. 7

Cost savings as a function of number of active routes in the bio-inspired network for the 15-city problem. The numbers next to each point denote the capacity of the new aircraft. Points without a number denote usage of the existing fleet (see color figure online).

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Fig. 8

Cost savings as a function of number of active routes in the bio-inspired network for the 20-city problem. The numbers next to each point denote the capacity of the new aircraft. Points without a number denote usage of the existing fleet (see color figure online).

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