Artificial Intelligence Methodologies in Air Transportation Network Routing and Scheduling

Rodin, Ervin; Washington University, St Louis. Department of Systems Science and Mathematics. Center for Optimization and Semantic Control

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

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Artificial Intelligence Methodologies in Air Transportation Network Routing and Scheduling

2000-04-14
By Rodin, Ervin

[PDF-202.16 KB]

English

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Details:

Creators:

Rodin, Ervin
Corporate Creators:

Washington University, St Louis. Department of Systems Science and Mathematics. Center for Optimization and Semantic Control
Corporate Contributors:

United States. Air Force Research Laboratory (University of New Mexico) ; United States. Air Force. Office of Science Research

United States. Air Force Research Laboratory (University of New Mexico) ; United States. Air Force. Office of Science Research Less -
Subject/TRT Terms:

[+]

Air Traffic Air Transportation Artificial Intelligence Aviation General Aviation Optimization Routes And Routing Scheduling
Resource Type:

Research Paper
Geographical Coverage:

United States
Corporate Publisher:

United States. Air Force. Office of Science Research
Abstract:

The reason for submitting a single report for two projects is that the graduate and undergraduate students who were supported by the second of these projects, actually worked on the subject matter of the first one. This project has been an ongoing one, in collaboration with the HQ/AMC/XPY at Scott AFB. The various joint projects undertaken are presented and discussed at frequent periodic meeting the personnel of HQ/AMC/XPY and members of the Center for Optimization and Semantic Control at Washington University.
Format:

PDF
Funding:

F49620-96-1-0151 ; F49620-96-1-0218

F49620-96-1-0151 ; F49620-96-1-0218 Less -
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:89bcb61fd2acbf86360db033074e91ca183d3003f9d1cec792526c8731e15988
Download URL:

https://rosap.ntl.bts.gov/view/dot/58678/dot_58678_DS1.pdf
File Type:

No Additional Files

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Artificial Intelligence Methodologies in Air Transportation Network Routing and Scheduling

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